automatic assessment technologies to support drums practicing

Automatic assessment technologies to support drums practicing

Amoroacutes Cortiella Maciagrave

Curs 2020-2021

Treball de Fi de Grau

Director Xavier Serra

GRAU EN ENGINYERIA EN SISTEMES AUDIOVISUALS

Bachelorrsquos Degree on Audiovisuals Systems EngineeringUniversitat Pompeu Fabra

Automatic assessment technologies tosupport drums practicing

Maciagrave Amoroacutes i Cortiella

Supervisor Xavier Serra

May 2021

Contents

1 Introduction 1

11 Motivation 1

12 Existing solutions 2

13 Identified challenges 3

131 Guitar vs drums 3

132 Dataset creation 4

133 Signal quality 4

14 Objectives 5

15 Project overview 5

2 State of the art 6

21 Signal processing 6

211 Feature extraction 6

212 Data augmentation 8

22 Sound event classification 9

221 Drums event classification 9

23 Digital sheet music 11

24 Software tools 11

241 Essentia 12

242 Scikit-learn 12

243 Lilypond 12

244 Pysimmusic 13

245 Music Critic 13

25 Summary 13

3 The 40kSamples Drums Dataset 14

31 Existing datasets 14

311 MDB Drums 15

312 IDMT Drums 15

32 Created datasets 16

321 Music school 16

322 Studio recordings 18


34 Drums events trim 23

35 Summary 23

4 Methodology 24

41 Problem definition 24

42 Drums event classifier 25


422 Training and validating 27

423 Testing 31

43 Music performance assessment 33

431 Visualization 33

432 Files used 35

5 Results 36

51 Tempo limitations 37

52 Saturation limitations 40

53 Evaluation of the assessment 43

6 Discussion and conclusions 47

61 Discussion of results 47

62 Further work 48

63 Work reproducibility 49

64 Conclusions 50

List of Figures 51

List of Tables 53

Bibliography 54

A Studio recording media 57

B Extra results 60

Acknowledgement

I would like to express my sincere gratitude to

bull Xavier Serra for supervising this project

bull Vsevolod Eremenko Eduard Vergeacutes and the MTG for helping me whenever I

have needed it

bull Sepe Martiacutenez and the Stereodosis team for helping me record a drums dataset

Abstract

This project is focused in the development of automated assessment tools for the

support of musical instrument learning concretely drums learning The state of

the art on music performance assessment has advanced in the last years and the

demand for online learning resources has increased The goal is to develop software

that listens to a student reading a music sheet with drums then evaluates the audio

recording giving some feedback on the tempo and reading accuracy First a review

of the previous work on different topics is made Then a drums event dataset

is created as well as a classifier and a performance assessment pipeline has been

developed focused on giving feedback through a visualization of the waveform in

parallel with the exercise score Results suggests that the classifier can generalize

with new audios despite this there is a large margin to improve the classification

results Furthermore there are some limitations in terms of maximum tempo to

classify correctly the events and the amount of saturation that the system can afford

to ensure a correct prediction Finally a discussion introduces the idea of a teacher

that follows the same process as the pipeline proposed thinking if this is a correct

approach to the problem and if this is a fair way to evaluate the system in a task

that we do differently with more information than one audio

Keywords Automatic assessment Signal processing Music information retrieval

Drums Machine learning

Chapter 1

Introduction

The field of sound event classification and concretely drums events classification has

improved the results during the last years and allows us to use this kind of technolo-

gies for a more concrete application like education support [1] The development of

automated assessment tools for the support of musical instrument learning has been

a field of study in the Music Technology Group (MTG) [2] concretely on guitar

performances implemented in the Pysimmusic project [3] One of the open paths

that proposes Eremenko et al [2] is to implement it with different instruments

and this is what I have done

11 Motivation

The aim of the project is to implement a tool to evaluate musical performances

specifically reading scores with drums One possible real application may be to sup-

port the evaluation in a music school allowing the teacher to focus on other items

such as attitude posture and more subtle nuances of performance If high accuracy

is achieved in the automatic assessment of tempo and reading a fair assessment of

these aspects can be ensured In addition a collaboration between a music school

and the MTG allows the use of a specific corpus of data from the educational insti-

tution program this corpus is formed by a set of music sheets and the recordings of

the performances

1

2 Chapter 1 Introduction

Besides this I have been studying drums for fourteen years and a personal motivation

emerges from this fact Learning an instrument is a process that does not only rely

on going to class there is an important load of individual practice apart of the class

indeed Having a tool to assess yourself when practicing would be a nice way to

check your own progress

12 Existing solutions

In terms of music interpretation assessment there are already some software tools

that support assessing several instruments Applications such as Yousician1 or

SmartMusic2 offer from the most basic notions of playing an instrument to a syllabus

of themes to be played These applications return to the students an evaluation that

tells which notes are correctly played and which are not but do not give information

about tempo consistency or dynamics and even less generates a rubric as a teacher

could develop during a class

There are specific applications that support drums learning but in those the feature

of automatic assessment disappears There are some options to get online drums

lessons such as Drumeo3 or Drum School4 but they only offer a list of videos impart-

ing lessons on improving stylistic vocabulary feel improvisation or technique These

applications also offer personal feedback from professional drummers and a person-

alized studying plan but the specific feature of automatic performance assessment

is not implemented

As mentioned in the Introduction automatic music assessment has been a field

of research at the MTG With the development of Music Critic5 an assessment

workflow is proposed and implemented This is useful as can be adapted to the

drums assessment task

1httpsyousiciancom2httpswwwsmartmusiccom3httpswwwdrumeocom4httpsdrumschoolappcom5httpsmusiccriticupfedu


13 Identified challenges

As mentioned in [2] there are still improvements to do in the field of music as-

sessment especially analyzing expressivity and with advanced level performances

Taking into account the scope of this project and having as a base case the guitar

assessment exercise from Music Critic some specific challenges are described below

131 Guitar vs drums

As defined in [4] a drumset is a collection of percussion instruments Mainly cym-

bals and drums even though some genres may need to add cowbells tambourines

pailas or other instruments with specific timbres Moreover percussion instruments

are splitted in two families membranophones and idiophones membranophones

produces sound primarily by hitting a stretched membrane tuning the membrane

tension different pitches can be obtained [5] differently idiophones produces sound

by the vibration of the instrument itself and the pitch and timbre are defined by

its own construction [5] The vibration aforementioned is produced by hitting the

instruments generally this hit is made with a wood stick but some genres may need

to use brushes hotrods or mallets to excite specific modes of the instruments With

all this in mind and as stated in [1] it is clear that transcribing drums having to

take into account all its variants and nuances is a hard task even for a professional

drummer With this said there is a need to simplify the problem and to limit the

instruments of the drumset to be transcribed

Returning to the assessment task guitars play notes and chords tuned so the way

to check if a music sheet has been read correctly is looking for the pitch information

and comparing it to the expected one Differently instruments that form a drumset

are mainly unpitched (except toms which are tuned using different scales and tuning

paradigms) so the differences among drums events are on the timbre A different

approach has to be defined in order to check which instrument is being played for

each detected event the first idea is to apply machine learning for sound event

classification


Along the project we will refer to the different instruments that conform a drumkit

with abbreviations In Table 1 the legend used is shown the combination of 2 or

more instruments is represented with a rsquo+rsquo symbol between the different tags

Instrument Kick Drum Snare Drum Floor tom Mid tom High tomAbbreviation kd sd ft mt ht

Instrument Hi-hat Ride cymbal Crash cymbalAbbreviation hh cy cr

Table 1 Abbreviationsrsquo legend

132 Dataset creation

Keeping in mind the last idea of the previous section if a machine learning approach

has to be implemented there is a basic need to obtain audio data of drums Apart

from the audio data proper annotations of the drums interpretations are needed in

order to slice them correctly and extract musical features of the different events

The process of gathering data should take into account the different possibilities that

offers a drumset in terms of timbre loudness and tone Several datasets should be

combined as well as additional recordings with different drumsets in order to have

a balanced and representative dataset Moreover to evaluate the assessment task

a set of exercises has to be recorded with different levels of skill

There is also the need to capture those sounds with several frequency responses in

order to make the model independent of the microphone Also those samples could

be processed to get variations of each of them with data augmentation processes

133 Signal quality

Regarding the assignment we have to take into account that a student will not be

able to record its interpretations with a setup as the used in a studio recording most

of the time the recordings will be done using the laptop or mobile phone microphone

This fact has to be taken into account when training the event classifier in order

to do data augmentation and introduce these transformations to the dataset eg

introducing noise to the samples or amplifying to get overload distortion

14 Objectives 5

14 Objectives

The main objective of this project is to develop a tool to assess drums interpretations

of a proposed music sheet This objective has to be split into the different steps of

the pipeline

bull Generate a correctly annotated drums dataset which means a collection of

audio drums recordings and its annotations all equally formatted

bull Implement a drums event sound classifier

bull Find a way to properly visualize drums sheets and their assessment

bull Propose a list of exercises to evaluate the technology

In addition having the code published in a public Github6 repository and uploading

the created dataset to Freesound7 and Zenodo8 will be a good way to share this work

15 Project overview

The next chapters will be developed as follows In chapter 2 the state of the art is

reviewed Focusing on signal processing algorithms and ways to implement sound

event classification ending with music sheet technologies and software tools available

nowadays In chapter 3 the creation of a drums dataset is described Presenting the

use of already available datasets and how new data has been recorded and annotated

In chapter 4 the methodology of the project is detailed which are the algorithms

used for training the classifier as well as how new submissions are processed to assess

them In chapter 5 an evaluation of the results is done pointing out the limitations

and the achievements Chapter 6 concludes with a discussion on the methods used

the work done and further work

6httpsgithubcom7httpsfreesoundorg8httpszenodoorg

Chapter 2

State of the art

In this chapter the concepts and technologies used in the project are explained

covering algorithm references and existing implementations First signal process-

ing techniques on onset detection and feature extraction are reviewed then sound

event classification field is presented and its relationship with drums event classifica-

tion Also the principal music sheet technologies and codecs are presented Finally

specific software tools are listed

21 Signal processing

211 Feature extraction

In the following sections sound event classification will be explained most of these

methods are based on training models using features extracted from the audio not

with the audio chunks indeed [6] In this section signal processing methods to get

those features are presented

Onset detection

In an audio signal an onset is the beginning of a new event it can be either a

single note a chord or in the case of the drums the sound produced by hitting one

or more instruments of the drumset It is necessary to have a reliable algorithm

6


that properly detects all the onsets of a drums interpretation With the onsets

information (a list of timestamps) the audio can be sliced to analyze each chunk

separately and to assess the tempo consistency

It is important to address the challenge in a psychoacoustical way as the objective

is to detect the musical events as a human will do In [7] the idea of perceptual

onset for percussive instruments is defined as a time interval between the physical

onset and the moment that the maximum level is reached In [8] many methods are

reviewed focusing on the differences of performance depending on the signal Non

Pitched Percussive instruments are better detected with temporal methods or high-

frequency content methods while Pitched Non Percussive instruments may need to

take into account changes of energy in the spectrum distribution as the onset may

represent a different note

The sound generated by the drums is mainly percussive (discarding brushesrsquo slow

patterns or malletrsquos build-ups on the cymbals) which means that is formed by a

short transient followed by a short decay there is no sustain As the transient is a

fast change of energy it implies a high-frequency content because changes happen

in a very little frame of time As recommended in [9] HFC method will be used

Timbre features

As described in [10] a feature denotes in some way a quantity or a value Features

extracted by processing the audio stream or transformations of that (ie FFT)

are called low-level descriptors these features have no relevant information from a

human point of view but are useful for computational processes [11]

Some low-level descriptors are computed from the temporal information for in-

stance the zero-crossing rate tells the number of times the signal crosses the zero

axis per second the attack time is the duration of the transient and temporal cen-

troid the energy distribution of an event during the time Other well known features

are the root median square of the signal or the high-frequency content mentioned

in section 211

8 Chapter 2 State of the art

Besides temporal features low-level descriptors can also be computed from the fre-

quency domain Some of them are spectral flatness spectral roll-off spectral slope

spectral flux ia

Nowadays Essentialsquos library offers a collection of algorithms that reliably extracts

the low-level descriptors aforementioned the function that englobes all the extrac-

tors is called Music extractor1

212 Data augmentation

Data augmentation processes refer to the optimization of the statistical representa-

tion of the datasets in terms of improving the generalization of the resultant models

These methods are based on the introduction of unobserved data or latent variables

that may not be captured during the dataset creation [12]

Regarding this technique applied to audio data signal processing algorithms are

proposed in [13] and [14] that introduces changes to the signals in both time and

frequency domains In these articles the goal is to improve accuracy on speech and

animal sound recognition although this could apply to drums event classification

The processes that lead best results in [13] and [14] were related to time-domain

transformations for instance time-shifting and stretching adding noise or harmonic

distortion compressing in a given dynamic range ia Other processes proposed

were focused on the spectrogram of the signal applying transformations such as

shifting the matrix representation setting to 0 some areas or adding spectrograms

of different samples of the same class

Presently some Python2 libraries are developed and maintained in order to do audio

data augmentation tasks For instance audiomentations3 and the GPU version

torch-audiomentations4

1httpsessentiaupfedustreaming_extractor_musichtml2httpswwwpythonorg3httpspypiorgprojectaudiomentations0604httpspypiorgprojecttorch-audiomentations


22 Sound event classification

Sound Event Classification is the task of detecting and recognizing sound events in

an audio stream [15] As described in [10] this task can be approached from two

sides on one hand the perceptual approach tries to extract the timbre similarity to

cluster sounds as how we perceive them on the other hand the taxonomic approach

is determined to label sound events as they are defined in the cultural or user biased

taxonomies In this project the focus is on the second approach as the task is to

classify sound events in the drums taxonomy (ie kick drum snare drum hi-hat)

Also in [] many classification methods are proposed Concretely in the taxonomy

approach machine learning algorithms such as K-Nearest Neighbors Support Vector

Machines or Neural Networks All of them using features extracted from the audio

data as explained in section 211

221 Drums event classification

This section is divided into two parts first presenting the state-of-the-art methods

for drum event classification and then the most relevant existing datasets This

section is mainly based on the article [1] as it is a review of the topic and encompasses

the core concepts of the project

Methods

Focusing on the taxonomic drums events classification this field has been studied for

the last years as in the Music Information Retrieval Evaluation eXchange5 (MIREX)

has been a proposed challenge since 20056 In [1] a review of the main methods

that have been investigated is done The authors collect different approaches such

as Recurrent Neural Networks proposed in [16] Non-Negative matrix factorization

proposed in [17] and others real-time based using MaxMSP7 as described in [18]

5httpswwwmusic-irorgmirexwikiMIREX_HOME6httpswwwmusic-irorgmirexwiki2005Audio_Drum_Detection_Results7httpscycling74comproductsmax


It is needed to mention that the proposed methods are focused on Automatic Drum

Transcription (ADT) of drumsets formed only by the kick drum snare drum and

hi-hat ADT field is intended to transcribe audio but in our case we have to check

if an audio event is or not the expected event this particularity can be used in our

favor as some assumptions can be made about the audio that has to be analyzed

Datasets

In addition to the methods and their combinations the data used to train the

system plays a crucial role As a result the dataset may have a big impact on the

generalization capabilities of the models In this section some existing datasets are

described

bull IDMT-SMT-Drums [19] Consists of real drum recordings containing only

kick drum snare drum and hi-hat events Each recording has its transcription

in xml format and is publicly avaliable to download8

bull MDB Drums [20] Consists of real drums recordings of a wide range of genres

drumsets and styles Each recording has two txt transcriptions for the classes

and subclasses defined in [20] (eg class Hi-hat Subclasses Closed hi-hat

open hi-hat pedal hi-hat) It is publicly avaliable to download9

bull ENST-Drums [21] Consists of real drum audio and video recordings of dif-

ferent drummers and drumsets Each recording has its transcription and some

of them include accompaniment audio It is publicly available to download10

bull DREANSS [22] Differently this dataset is a collection of drum recordings

datasets that have been annotated a posteriori It is publicly available to

download11

Electronic drums datasets have not been considered as the student assignment is

supposed to be recorded with a real drumset8httpswwwidmtfraunhoferdeenbusiness_unitsm2dsmtdrumshtml9httpsgithubcomCarlSouthallMDBDrums

10httpspersotelecom-paristechfrgrichardENST-drums11httpswwwupfeduwebmtgdreanss


23 Digital sheet music

Several music sheet technologies have been developed since the first scorewriter

programs from the 80s Proprietary softwares as Finale12 and Sibelius13 or open-

source software as MuseScore14 and LilyPond15 are some options that can be used

nowadays to write music sheets with a computer

In terms of file format Sibelius has its encrypted version that can only be read and

written with the software it can also write and read MusicXML16 files which are

not encrypted and are similar to an HTML file as it contains tags that define the

bars and notes of the music sheet this format is the standard for exchanging digital

music sheet

Within Music Criticrsquos framework the technology used to display the evaluated score

is LilyPond it can be called from the command line and allows adding macros that

change the size or color of the notes The other particularity is that it uses its own

file format (ly) and scores that are in MusicXML format have to be converted and

reviewed

24 Software tools

Many of the concepts and algorithms aforementioned are already developed as soft-

ware libraries this project has been developed with Python and in this section the

libraries that have been used are presented Some of them are open and public and

some others are private as pysimmusic that has been shared with us so we can use

and consult it In addition all the code has been developed using a tool from Google

called Collaboratory17 it allows to write code in a jupyter notebook18 format that

is agile to use and execute interactively

12httpswwwfinalemusiccom13httpswwwavidcomsibelius14httpsmusescoreorg15httpslilypondorg16httpswwwmusicxmlcom17httpscolabresearchgooglecom18httpsjupyterorg


241 Essentia

Essentia is an open-source C++ library of algorithms for audio and music analysis

description and synthesis [23] it can also be installed as a Python-based library

with the pip19 command in Linux or compiling with certain flags in MacOS20 This

library includes a collection of MIR algorithms it is not a framework so it is in the

userrsquos hands how to use these processes Some of the algorithms used in this project

are music feature extraction onset detection and audio file IO

242 Scikit-learn

Scikit-learn21 is an open-source library for Python that integrates machine learning

algorithms for regression classification and clustering as well as pre-processing and

dimensionality reduction functions Based on NumPy22 and SciPy23 so its algorithms

are easy to adapt to the most common data structures used in Python It also allows

to save and load trained models to do inference tasks with new data

243 Lilypond

As described in section 23 LilyPond is an open-source songwriter software with

its file format and language It can produce visual renders of musical sheets in

PNG SVG and PDF formats as well as MIDI files to listen to the compositions

LilyPond works on the command line and allows us to introduce macros to modify

visual aspects of the score such as color or size

It is the digital sheet music technology used within Music Criticrsquos framework as

allows to embed an image in the music sheet generating a parallel representation of

the music sheet and a studentrsquos interpretation

19httpspypiorgprojectpip20httpsessentiaupfeduinstallinghtml21httpsscikit-learnorg22httpsnumpyorg23httpswwwscipyorgscipylibindexhtml

25 Summary 13

244 Pysimmusic

Pysimmusic is a private python library developed at the MTG It offers tools to

analyze the similarity of musical performances and uses libraries such as Essentia

LilyPond FFmpeg24 ia Pysimmusic contains onset detection algorithms and a

collection of audio descriptors and evaluation algorithms By now is the main eval-

uation software used in Music Critic to compare the recording submitted with the

reference

245 Music Critic

Music Critic is a project from the MTG intended to support technologies for online

music education facilitating the assessment of student performances25

The proposed workflow starts with a student submitting a recording playing the

proposed exercise Then the submission is sent to the Music Criticrsquos server where

is analyzed and assessed Finally the student receives the evaluation jointly with

the feedback from the server

25 Summary

Music information retrieval and machine learning have been popular fields of study

This has led to a large development of methods and algorithms that will be crucial

for this project Most of them are free and open-source and fortunately the private

ones have been shared by the UPF research team which is a great base to start the

development

24httpswwwffmpegorg25httpswwwupfeduwebmtgtech-transfer-asset_publisherpYHc0mUhUQ0G

contentid229860881maximizedYJrB-usp7YV

Chapter 3

The 40kSamples Drums Dataset

As stated in section 132 having a well-annotated and balanced dataset is crucial to

get proper results In this section the 40kSamples Drums Dataset creation process is

explained first focusing on how to process existing datasets such as the mentioned

in 221 Secondly introducing the process of creating new datasets with a music

school corpus and a collection of recordings made in a recording studio Finally

describing the data augmentation procedure and how the audio samples are sliced

in individual drums events In Figure 1 we can see the different procedures to unify

the annotations of the different datasets while the audio does not need any specific

modification

31 Existing datasets

Each of the existing datasets has a different annotation format in this section the

process of unifying them will be explained as well as its implementation (see note-

book Dataset_formatUnificationipynb1) As the events to take into account

can be single instruments or combinations of them the annotations have to be for-

matted to show that events properly None of the annotations has this approach

so we have written a function that filters the list and joins the events with a small

1httpsgithubcomMaciACtfg_DrumsAssessmentblobmasterDataset_formatUnificationipynb

14


difference of time meaning that they are played simultaneously

Music school Studio REC IDMT Drums MDB Drums

audio + txt

Sibelius to MusicXML

MusicXML parser to txt

Write annotations

AnnotationsAudio

Figure 1 Datasets pre-processing

311 MDB Drums

This dataset was the first we worked with the annotation format in txt was a key

factor as it was easy to read and understand As the dataset is available in Github2

there is no need to download it neither process it from a local drive As shown in

the first cells of Dataset_formatUnificationipynb data from the repository can

be retrieved with a Python wrapper of the Github API3

This dataset has two annotations files depending on how deep the taxonomy used

is [20] In this case the generic class taxonomy is used as there is no need to

differentiate styles when playing a given instrument (ie single stroke flam drag

ghost note)

312 IDMT Drums

Differently to the previous dataset this one is only available downloading a zip

file4 It also differs in the annotation file format which is xml Using the Python

2httpsgithubcomCarlSouthallMDBDrums3httpspypiorgprojectgithubpy4httpswwwidmtfraunhoferdeenbusiness_unitsm2dsmtdrumshtml

16 Chapter 3 The 40kSamples Drums Dataset

package xmltodict5 in the second part of Dataset_formatUnificationipynb the

xml files are loaded as a Python dictionary and converted to txt format

32 Created datasets

In order to expand the dataset with more variety of samples other methods to get

data have been explored On one hand with audio data that has partial annotations

or some representation that is not data-driven such as a music sheet that contains

a visual representation of the music but not a logic annotation as mentioned in

the previous section On the other hand generating simple annotations is an easy

task so drums samples can be recorded standalone to create data in a controlled

environment In the next two sections these methods are described

321 Music school

A music school has shared its docent material with the MTG for research purposes

ie audio demos books in pdf format music sheet in Sibelius format As we can

see in Figure 1 the annotations from the music school corpus are in Sibelius format

this is an encrypted representation of the music sheet that can only be opened with

the Sibelius software The MTG has shared an AVID license which includes the

Sibelius software so we were able to convert the sib files to musicxml MusicXML

is not encrypted and allows to open it and read so a parser has been developed to

convert the MusicXML files to a symbolic representation of the music sheet This

representation has been inspired by [24] which proposes a system to represent chords

MusicXML parser

As mentioned in section 23 MusicXML format is based on ordering the visual

information with tags creating a tree structure of nested dictionaries In the first cell

of XML_parseripynb6 two functions are defined ConvertXML2Annotation reads

the musicxml file and gets the general information of the song (ie tempo time

5httpspypiorgprojectxmltodict6httpsgithubcomMaciACtfg_DrumsAssessmentblobmasterXML_parseripynb


measure title) then a for loops throughout all the bars of the music sheet checking

whereas the given bar is self-defined the repetition of the previous one or the begin

or end of a repetition in the song (see Figure 2) in the self-defined bar case the bar

indeed is passed to an auxiliar function which parses it getting the aforementioned

symbolic representation

Figure 2 Sample drums score from music school drums grade 1

In Figure 2 we can see a staff in which the first bar has been written and the three

others have a symbol that means rsquorepetition of the previous barrsquo moreover the

bar lines at the beginning and the end represents that these four bars have to be

repeated therefore this line in the music score represents an interpretation of eight

bars repeating the first one

The symbolic representation that we propose is based in [24] defines each bar with

a string this string contains the representations of the events in the bar separated

with blank spaces Each of the events has two dots () to separate the figure (ie

quarter note half note whole note) from the note or notes of the event which

are separated by a dot () For instance the symbolic representation of the first bar

in Figure 2 is F4A44 F4A44 F4A44 F4A44

In addition to this conversion in parse_one_measure function from XML_parser

notebook each measure is checked to ensure that fully represents the bar This

means that the sum of the figures of the bar has to be equal to the defined in the

time measure the sum of the events in a 44 bar has to be equal to four quarter

notes

Symbolic notation to unified annotation format

As we can see in Figure 1 once the music scores are converted to the symbolic

representation the last step is to unify the annotations with the used in sections 31


This process is made in the last cells of Dataset_formatUnification7 notebook

A dictionary with the translation of the notes to drums instrument is defined so

the note is already converted Differently the timestamp of each event has to be

computed based on the tempo of the song and the figure of each event this process

is made with the function get_time_steps_from_annotations8 which reads the

interpretation in symbolic notation and accumulates the duration of each event

based on the figure and the tempo

322 Studio recordings

At this point of the dataset creation we realized that the already existing data

was so unbalanced in terms of instances per class some classes had around two

thousand samples while others had only ten This situation was the reason to

record a personalized dataset to balance the overall distribution of classes as well

as exercises with different accuracy when reading simulating students with different

skill levels

The recording process took place on April 16 and 17 at Stereodosis Estudio9 (Sants

Barcelona) the first day was intended to mount the drumset and the microphones

which are listed in Table 2 in Figure 3 the microphone setup is shown differently

to the standard setup in which each instrument of the set has its microphone this

distribution of the microphones was intended to record the whole drumset with

different frequency responses

The recording process was divide into two phases first creating samples to balance

the dataset used to train the drums event classifier (called train set) Then recording

the studentsrsquo assignment simulation to test the whole system (called test set)


8httpsgithubcomMaciACtfg_DrumsAssessmentblobe81be958101be005cda805146d3287eec1a2d5a4scriptsdrumspyL9

9httpswwwstereodosiscom


Microphone Transducer principleBeyerdynamic TG D70 Dynamic

Shure PG52 DynamicShure SM57 Dynamic

Sennheiser e945 DynamicAKG C314 CondenserAKG C414 CondenserShure PG81 CondenserSamson C03 Condenser

Table 2 Microphones used

Figure 3 Microphone setup for drums recording

Train set

To limit the number of classes we decided to take into account only the classes

that appear in the music school subset this decision was motivated by the idea of

assessing the songs from the books so only classes of the collection of songs were

needed to train the classifier In Figure 4 the distribution of the selected classes

before the recordings is shown note that is in logarithmic scale so there is a large

difference among classes


Figure 4 Number of samples before Train set recording

To organize the recording process we designed 3 different routines to record depend-

ing on the class and the number of samples already existing a different routine was

recorded These routines were designed trying to represent the different speeds dy-

namics and interactions between instruments of a real interpretation In Appendix

A the routines scores are shown to write a generic routine a two lines stave is used

the bottom line represents the class to be recorded and the top line an auxiliary

one The auxiliary classes are cymbals concretely crashes and rides whose sound

remains a long period of time and its tail is mixed with the subsequent sound events

bull Routine 1 (Fig 31) This routine is intended for the classes that do not include

a crash or ride cymbal and has a small number of classes (ie lt500)

bull Routine 2 (Fig 32) This routine does not include auxiliary events as it is

intended for classes that include crash or ride cymbal whose interaction with

itself is intrinsic

bull Routine 3 (Fig 33) This is a short version of routine 1 which only repeats

each bar two times instead of four is intended for classes which not include a

crash or ride cymbal and has a large number of classes (ie gt500)


Routines 1 and 3 were recorded only one time as we had only one instrument of each

of the classes differently routine 2 was recorded two times for each cymbal as we

was able to use more instances of them different cymbals configurations used can

be seen in Appendix A in Figures 34 35 and 36

After the Train set recording the number of samples was a little more balanced as

shown in Figure 5 all the classes have at least 1500 samples

0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh

recorded before record

Figure 5 Number of samples after Train set recording

Test set

The test set recording tried to simulate different students performing the same song

in the same drumset to do that we recorded each song of the music school Drums

Grade Initial and Grade 1 playing it correctly and then making mistakes in both

reading and rhythmic ways After testing with these recordings we realized that we

were not able to test the limits of the assessment system in terms of tempo or with

different rhythmic measures So we proposed two exercises of groove reading in 44

and in 128 to be performed with different tempos these recordings have been done

in my study room with my laptoprsquos microphone



As described in section 212 data augmentation aims to introduce changes to the

signals to optimize the statistical representation of the dataset To implement this

task the aforementioned Python library audiomentations is used

The library Audiomentations has a class called Compose which allows collecting

different processing functions assigning a probability to each of them Then the

Compose instance can be called several times with the same audio file and each time

the resulting audio will be processed differently because of the probabilities In

data_augmentationipynb10 a possible implementation is shown as well as some

plots of the original sample with different results of applying the created Compose

to the same sample an example of the results can be listened in Freesound11

The processing functions introduced in the Compose class are based in the proposed

in [13] and [14] its parameters are described

bull Add gaussian noise with 70 of probability

bull Time stretch between 08 and 125 with a 50 of probability

bull Time shift forward a maximum of 25 of the duration with 50 of probability

bull Pitch shift plusmn2 semitones with a 50 of probability

bull Apply mp3 compression with a 50 of probability

10httpsgithubcomMaciACtfg_DrumsAssessmentblobmasterdata_augmentationipynb

11httpsfreesoundorgpeopleMaciaACpacks32213


34 Drums events trim

As will be explained in section 421 the dataset has to be trimmed into individual

files to analyze them and extract the low-level descriptors In the Dataset_feature

Extractionipynb12 notebook this process has been implemented slicing all the

audios with its annotations each dataset separately to sight-check all the resultant

samples and detect better which annotations were not correct

35 Summary

To summarize a drums samples dataset has been created the one used in this

project will be called the 40k Samples Drums Dataset Nonetheless to share this

dataset we have to ensure that we are fully proprietary of the data which means

that the samples that come from IDMT MDBDrums and MusicSchool datasets

cannot be shared in another dataset Alternatively we will share the 29k Samples

Drums Dataset formed only by the samples recorded in the studio This dataset will

be available in Zenodo13 to download the whole dataset at once and in Freesound

some selected samples are uploaded in a pack14 to show the differences among mi-

crophones

12httpsgithubcomMaciACtfg_DrumsAssessmentblobmasterDataset_featureExtractionipynb

13httpszenodoorgrecord4958592YMmNXW4p5TZ14httpsfreesoundorgpeopleMaciaACpacks32397

Chapter 4

Methodology

In this chapter the methodologies followed in the development of the assessment

pipeline are explained In Figure 6 the proposed pipeline diagram is shown it is

inspired by [2] Each box of the diagram refers to a section in this chapter so the

diagram might be helpful to get a general idea of the problem when explaining each

process

The system is divided into two main processes First the top boxes correspond to

the training process of the model using the dataset created in the previous chapter

Secondly the bottom row shows how a student submission is processed to generate

some feedback This feedback is the output of the system and should give some

indications to the student on how has performed and how can improve

41 Problem definition

To check if a student reads correctly a music sheet we need some tool to tag which

instruments of the drumset is playing for each detected event This leads us to

develop and train a Drums events classifier if this tool ensures a good accuracy

when classifying (ie lt95) we will be able to properly assess a studentrsquos recording

If the classifier has not enough accuracy the system will not be useful as we will not

be able to differentiate among errors from the student and errors from the classifier

24


Assessments

Music Scores

Studentsrsquo performances

Annotations

Audio recordings

Dataset

Feature extraction

Drums event classifier training

Performanceassessment

training

Feature extraction


inference

New studentrsquos recording

Visualization Performancefeedback

Figure 6 Proposed pipeline for a drums performance assessment system inspiredby [2]

For this reason the project has been mainly focused on developing the aforemen-

tioned drums event classifier and a proper dataset So developing a properly as-

sessed dataset of drums interpretations has not been possible nor the performance

assessment training Despite this the feedback visualization has been developed as

it is a nice way to close the pipeline and get some understandable results moreover

the performance feedback could be focused on deterministic aspects as telling the

student if is rushing or slowing in relation to a given tempo

42 Drums event classifier

As already mentioned this section has been the main load of work for this project

because of the dependence of a correct automatic transcription in order to do a

reliable assessment The process has been divided into 3 main parts extracting

26 Chapter 4 Methodology

the musical features training and validating the model in an iterative process and

finally validating the model with totally new data


The feature extraction concept has been explained in Section 211 and has been

implemented using the MusicExtractor()1 method from Essentiarsquos library

MusicExtractor() method has to be called passing as a parameter the window and

hope sizes that will be used to perform the analysis as well as a filename of the event

to be analyzed The function extract_MusicalFeatures()2 has been implemented

to loop a list of files and analyze each of them to add the extracted features to a

csv file jointly with the class of each drum event At this point all the low-level

features were extracted both mean and standard deviation were computed across

all the frames of the given audio filename The reason was that we wanted to check

which features were redundant or meaningful when training the classifier

As mentioned in section 34 the fact that MusicExtractor() method has to be

called with a filename not an audio stream forced us to create another version of

the dataset which had each event annotated in a different audio file with the corre-

spondent class label as a filename Once all the datasets were properly sliced and

sight-checked the last cell of the notebook were executed with the correspondent

folder names (which contains all the sliced samples) and the features saved in differ-

ent csv one for each dataset3 Adding the number of instances in all the csv files

we get 40228 instances with 84 features and 1 label

1httpsessentiaupfedureferencestd_MusicExtractorhtml2httpsgithubcomMaciACtfg_DrumsAssessmentblobe81be958101be005cda805146d3287eec1a2d5a4

scriptsfeature_extractionpyL63httpsgithubcomMaciACtfg_DrumsAssessmenttreemasterdataslices

features


422 Training and validating

As mentioned in section 22 some authors have proposed machine learning algo-

rithms such as Support Vector Machines (SVM) and K-Nearest Neighbours (KNN)

to do sound event classification also some authors have developed more complex

methods for drums event classification The complexity of these last methods made

me choose the generic ones also to try if it were a good way to approach the problem

as there is no literature concretely on drums event classification with SVM or KNN

The iterative process of training and validating the aforementioned methods has

been the main reference when designing the 40k Drums samples dataset the first

times we tried the models we were working with the classes distribution of Figure

4 as commented this was a very unbalanced dataset and we were evaluating the

classification inference with the accuracy formula 41 that did not take into account

the unbalance in the dataset The accuracy computation was around 92 but the

correct predictions were mainly on the large classes as shown in Figure 7 some

classes had very low accuracy (even 0 as some classes has 10 samples 7 used to

train an 3 to validate which are all bad predicted) but having a little number of

instances affects less to the accuracy computation

accuracy(y y) =1

nsamples

nsamplesminus1sumi=0

1(yi = yi) (41)

Otherwise the proper way to compute the accuracy in this kind of datasets is the

balanced accuracy it computes the accuracy for each class and then averages the

accuracy along with all the classes as in formula 42 where wi represents the weight

of each class in the dataset This computation lowered the result to 79 which was

not a good result

wi =wisum

j 1(yj = yi)wj

(42)

balanced-accuracy(y y w) =1sumwi

sumi

1(yi = yi)wi


Figure 7 Confusion matrix after training with the dataset in Figure 4

Another widely used accuracy indicator for classification models is the f-score which

combines the precision and the recall of the model in one measure as in formula

43 Precision is computed as the number of correct predictions divided by the total

number of predictions and recall is the number of correct predictions divided by the

total number of predictions that should be correct for a given class

F_measure =precisiontimes recallprecision+ recall

(43)

Having these results led us to the process of recording a personalized dataset to

extend the already existing (See section 322) With this new distribution the

results improved as shown in Figure 8 as well as better balanced accuracy and f-

score (both 89) Until this point we were using both KNN and SVM models to

compare results and the SVM performed always 10 better at least so we decided

to focus on the SVM and its hyper-parameter tunning


Figure 8 Confusion matrix after training with the dataset in Figure 5 and parameterC = 1

The C parameter in a support vector machine refers to the regularization this

technique is intended to make a model less sensitive to the data noise and the

outliers that may not represent the class properly When increasing this value to

10 the results improved among all the classes as shown in Figure 9 as well as the

accuracy and f-score (both 95)

At that point the accuracy of the model was pretty good but the 88 on the snare

drum class was somehow a problem as is one of the most used instruments in the

drumset jointly with the hi-hat and the kick drum So I tried the same process

with the classes that include only the three mentioned instruments (ie hh kd sd

hh+kd hh+sd kd+sd and hh+kd+sd) Reducing the number of classes improved

the overall accuracy and f-score to 977 and concretely the sd accuracy to 96 as

shown in Figure 10



Figure 10 Confusion matrix after training with the dataset in Figure 5 and param-eter C = 10 but only hh sd and kd classes


The implementation of the training and validating iterative process has been de-

veloped in the Classifier_trainingipynb4 notebook First loading the csv files

with the features extracted in Dataset_featureExtractionipynb then depend-

ing on which subset of classes will be used the correspondent instances and filtered

and to remove redundant features the ones with a very low standard deviation are

deleted (ie std_dev lt 000001) As the SVM works better when data is normalized

the standard scaler is used to move all the data distributions around 0 and ensuring

a standard deviation of 1

In the next cells the dataset is split into train and validation sets and the training

method from the SVM of sklearn is called to perform the training when the models

are trained the parameters are dumped in a file to load the model a posteriori and

be able to apply the knowledge learned to new data This process was so slow on

my computer so we decided to upload the csv files to Google Drive and open the

notebook with Google Collaboratory as it was faster and is a key feature to avoid

long waiting times during the iterative train-validate process In the last cells the

inference is made with the validation set and the accuracy is computed as well as

the confusion matrix plotted to get an idea of which classes are performing better

423 Testing

Testing the model introduces the concept of onset detection until now all the slices

have been created using the annotations but to assess a new submission from

a student we need to detect the onsets and then slice the events The function

SliceDrums_BeatDetection5 does both tasks as explained in section 211 there

are many methods to do onset detection and each of them is better for a different

application In the case of drums we have tested the rsquocomplexrsquo method which finds

changes in the frequency domain in terms of energy and phase and works pretty

well but when the tempo increase there are some onsets that are not correctly de-

tected for this reason we finally implemented the onset detection with the HFC4httpsgithubcomMaciACtfg_DrumsAssessmentblobmasterClassifier_

trainingipynb5httpsgithubcomMaciACtfg_DrumsAssessmentblob9422e71a998d3cd0a6c7f03e92a8b0c6f6dac869

scriptsdrumspyL45


method This method computes for each window the HFC as in equation 44 note

that high-frequency bins (k index) weights more in the final value of the HFC

HFC(n) =sumk

|Xk[n]|2lowastk (44)

Moreover the function plots the audio waveform jointly with the onsets detected to

check if it has worked correctly after each test In Figures 11 and 12 we can see two

examples of the same music sheet played at 60 and 220 bpm in both cases all the

onsets are correctly detected and no false detection occurs

Figure 11 Onsets detected in a 60bpm drums interpretation


With the onsets information the audio can be trimmed in the different events the

order is maintained with the name of the file so when comparing with the expected

events can be mapped easily The audios are passed to the extract_MusicalFeatures()

function that saves the musical features of each slice in a csv


To predict which event is each slice the models already trained are loaded in this new

environment and the data is pre-processed using the same pipeline as when training

After that data is passed to the classifier method predict() which returns for each

row in the data the predicted event The described process is implemented in the first

part of Assessmentipynb6 the second part is intended to execute the visualization

functions described in the next section

43 Music performance assessment

Finally as already commented the assessment part has been focused on giving visual

feedback of the interpretation to the student As the drums classifier has taken so

much time the creation of a dataset with interpretations and its grades has not been

feasible A first approximation was to record different interpretations of the same

music sheet simulating different levels of skills but grading it and doing all the

process by ourselves was not easy apart from that we tended to play the fragments

good or bad it was difficult to simulate intermediate levels and be consistent with

the proposed ones

So the implemented solution generates an image that shows to the student if the

notes of the music sheet are correctly read and if the onsets are aligned with the

expected ones

431 Visualization

With the data gathered in the testing section feedback of the interpretation has

to be returned Having as a base implementation the solution of my companion

Eduard Vergeacutes7 and thanks to the help of Vsevolod Eremenko8 in the last cell of

the notebook Assessmentipynb the visualization is done

First the LilyPond file paths are defined Then for each of the submissions the

audio is loaded to generate the waveform plot

6httpsgithubcomMaciACtfg_DrumsAssessmentblobmasterAssessmentipynb7httpsgithubcomEduardVergesFranchU151202_VA_FinalProject8httpsgithubcomseffkaForMacia


To do so the function save_bar_plot()9 is called passing the lists of detected and

expected onsets the waveform and the start and end of the waveform (this comes

from the lilypond filersquos macro) To properly plot the deviations in the code we are

assuming that the interpretation starts four beats after the beginning of the audio

In Figures 13 and 14 the result of save_bar_plot() for two different submissions is

shown The black lines in the bottom of the waveform are the detected onsets while

the cyan lines in the middle are the expected onsets when the difference between

the two values increase the area between them is colored with a traffic light code

(green good to red bad)

Figure 13 Onset deviation plot of a good tempo submission

Figure 14 Onset deviation plot of a bad tempo submission

Once the waveform is created it is embedded in a lambda function that is called from

the LillyPond render But before calling the LillyPond to render the assessment of

the notes has to be done In function assess_notes()10 the expected and predicted

events are passed with a comparison a list of booleans is created but with 0 in the

False and 1 in the True index then the resulting list is iterated and the 0 indices

are checked because most of the classification errors fail in one of the instruments

to be predicted (ie instead of hh+sd it is predicting sd) These cases are considered

partially correct as the system has to take into account its errors the indices in

which one of the instruments is correctly predicted and it is not a hi-hat (we are

9httpsgithubcomMaciACtfg_DrumsAssessmentblob2aaf0dbdd1f026dfebfba65eaac9fcd24a8629afscriptsvisualizationpyL112

10httpsgithubcomMaciACtfg_DrumsAssessmentblob2aaf0dbdd1f026dfebfba65eaac9fcd24a8629afscriptsdrumspyL88


considering it more important to get right the snare and kick reading than a hi-hat

which is present in all the events) the value is turned to 075 (light green in the color

scale) In Figure 15 the different feedback options are shown green notes mean

correct light green means partially correct and red means incorrect

Figure 15 Example of coloured notes

With the waveform the notes assessed and the LilyPond template the function

score_image()11 can be called This function renders the LilyPond template jointly

with the waveform previously created this is done with the LilyPond macros

On one hand before each note on the staff the keyword color() size()

determines that the color and size of the note depends on an external variable (the

notes assessed) and in the other hand after the first note of the staff the keyword

eps(1150 16) indicates on which beat starts to display the waveform and

on which ends in this case from 0 to 16 in a 44 rhythm is 4 bars and the other

number is the scale of the waveform and allows to fit better the plot with the staff

432 Files used

The assessment process of an exercise needs several files first the annotations of the

expected events and their timesteps this is found in the txt file that has been already

mentioned in the 311 section then the LilyPond file this one is the template write

in LilyPond language that defines the resultant music sheet the macros to change

color and size and to add the waveform are defined when extracting the musical

features each submission creates its csv file to store the information and finally

we need of course the audio files with the submission recorded to be assessed


Chapter 5

Results

At this point the system has been developed and the classifier trained so we can do

an evaluation of the results to check if it works correctly and is useful to a student to

learn also to test which are the limits regarding the audio signal quality and tempo

The tests have been done with two different exercises recorded with a computer

microphone and played at a different tempo starting at 60 bpm and adding 40

bpm until 220 bpm The recordings with good tempo and good reading have been

processed adding 6dB until an accumulate of +30 dB

In this chapter and Appendix B all the resultant feedback visualizations are shown

The audio files can be listened in Freesound a pack1 has been created Some of

them will be commented on and referenced in further sections the rest are extra

results

As the High frequency content method works perfectly there are no limitations nor

errors in terms of onset detection all the tests have an f-measure of 1 detecting all

the expected events without detecting any false positive


36


51 Tempo limitations

One of the limitations of the system is the tempo of the exercise the accuracy drops

when the tempo increases Having as a reference the Figures that show good reading

which all notes should be in green or light green (ie Figures 16 17 18 19 20

21 and 22) we can count how many are correct or partially correct to punctuate

each case a correct prediction weights 10 a partially correct weights 05 and an

incorrect 0 the total value is the mean of the weighted result of the predictions

Figure 16 Good reading and good tempo Ex 1 60 bpm



In Table 3 we can see that by increasing the tempo of exercise 1 the accuracy of the

classifier decreases it may be because increasing the tempo decreases the spacing

between events and consequently the duration of each event which leads to fewer

38 Chapter 5 Results



values to calculate the mean and standard deviation when extracting the timbre

characteristics As stated in the Law of Large numbers [25] the larger the sample

the closer the mean is to the total population mean In this case having fewer values

in the calculation creates more outliers in the distribution which tends to scatter

Tempo Correct Partially OK Incorrect Total60 25 7 0 089100 24 8 0 0875140 24 7 1 086180 15 9 8 061220 12 7 13 048

Table 3 Results of exercise 1 with different tempos


Regarding the 128 exercise (Figures 21 and 22) we were not able to record faster

than 100 bpm But in 128 the quarter notes equivalent in tempo is 300 quarter

note per minute similarly to the 140 bpm on 44 which quarter note per minute

temporsquos is 280 The results on 128 (Table 4) are also better because there are more

rsquoonly hi hatrsquo events which are better predicted




Tempo Correct Partially OK Incorrect Total60 39 8 1 089100 37 10 1 0875


52 Saturation limitations

Another limitation to the system is the saturation of the signal submitted Hearing

the submissions the hi-hat events are recorded with less amplitude than the snare

and kick events for this reason we think that the classifier starts to fail at +18dB

As can be seen in Tables 5 and 6 the same counting scheme as in the previous section

is done with Figure 23 and Figure 24 The hi-hat is the last waveform to saturate

and at this gain level the overall waveform is so clipped leading to a high-frequency

content that is predicted as a hi-hat in all the cases

Level Correct Partially OK Incorrect Total+0dB 25 7 0 089+6dB 23 9 0 086+12dB 23 9 0 086+18dB 24 7 1 086+24dB 18 5 9 064+30dB 13 5 14 048

Table 5 Results of exercise 1 at 60 bpm with different amplification levels




Figure 23 Good reading and good tempo Ex 1 60 bpm accumulating +6dB ateach new staff




53 Evaluation of the assessment

Until now the evaluation of results has been focused on the drums event classifier

accuracy but we think that is also important to evaluate if the system can assess

properly a studentrsquos submission

As shown in Figures 25 and 26 if the student does not play the first beat or some of

the beats are not read the system can map the rest of the events to the expected in

the correspondent onset time step This is due to a checking done in the assessment

which assumes that before starting the first beat there is a count-back of one bar

and the rest of the beats have to be after this interval

Figure 25 Bad reading and bad tempo Ex 1 100 bpm


To evaluate the assessment we will proceed as in previous sections counting the

number of correct predictions but now in terms of assessment The analyzed results

will be the rsquoBad reading good temporsquo ones shown in Figures 27 28 and 29


Figure 27 Bad reading and good tempo Ex 1 starts on 60 bpm and adds 60bpmat each new staff


Figure 28 Bad reading and good tempo Ex 2 60 bpm


On Tables 7 and 8 the counting is summarized and works as follows we count a

correct assessment if the note is green or light green and the event is the one in the

music score or if the note is red and the event is not the one in the music score

The rest of the cases will be counted as incorrect assessments The total value is

the number of correct assessments over the total number of events


Tempo Correct assessment Incorrect assessment Total60 32 0 1100 32 0 1140 32 0 1180 25 7 078220 22 10 068

Table 7 Assessment result of a bad reading with different tempos 44 exercise

Tempo Correct assessment Incorrect assessment Total60 47 1 098100 45 3 09


We can see that for a controlled environment and low tempos the system performs

pretty well the assessment based on the predictions This can be helpful for a student

to know which parts of the music sheet are well ridden and which not Also the

tempo visualization can help the student to recognize if is slowing down or rushing

when reading the score as can be seen in Figure 30 the onsets detected (black lines

in the bottom part of the waveform) are mostly behind the correspondent expected

onset

Figure 30 Good reading and bad tempo Ex 1 100 bpm

Chapter 6

Discussion and conclusions

At this point all the work of the project has been done and the results have been

analyzed In this chapter a discussion is developed about which objectives have been

accomplished and which not Also a set of further improvements is given and a final

thought on my work and my apprenticeship The chapter ends with an analysis of

how reusable and reproducible is my work

61 Discussion of results

Having in mind all the concepts explained along with this document we can now list

them defining the completeness and our contributions

Firstly the creation of the 29k Samples Drums Dataset is now publicly available and

downloadable from Freesound and Zenodo Apart from being used in this project

this dataset might be useful to other researchers and students in their projects

The dataset indeed is useful in order to balance datasets of drums based on real

interpretations as the class distribution of these interpretations is very unbalanced

as explained with the IDMT and MDB drums datasets

Secondly a drums event classifier with a machine learning approach has been pro-

posed and trained with the aforementioned dataset One of the reasons for using

this approach to predict the events was that there was no literature focused on

47

48 Chapter 6 Discussion and conclusions

classifying drums events in this manner As the results have shown more complex

methods based on the context might be used as the ones proposed in [16] and [17]

It is important to take into account that the task that the model is trained to do

is very hard for a human being able to differentiate drums events in an individual

drum sample without any context is almost impossible even for a trained ear as my

drums teacher or mine

Thirdly a review of the different music sheet technologies has been done as well as

the development of a MusicXML parser This part took around one month to be

developed and from my point of view it was a great way to understand how these

file formats work and how can be improved as they are majorly focused on the

visualization not the symbolic representation of events and timesteps

Finally two exercises in different time signatures have been proposed to demonstrate

the functionality of the system As well as tests of these exercises have been recorded

in a different environment than the 30k Samples Drums Dataset It would be fine

to get recordings in different spaces and with different drumsets and microphones

to test more exhaustively the system

62 Further work

In terms of the dataset created it could be larger It could be expanded with

different drumsets tuning differently each drumset using different sticks to hit the

instruments and even different people playing This could introduce more variance

in the drums sample dataset Moreover on June 9th 2021 a paper about a large

drums datasets with MIDI data was presented [26] in the ICASSP 20211 This new

dataset could be included in the training process as the authors state that having a

large-scale dataset improves the results of the existing models

Regarding the classification model it is clear that needs improvements to ensure the

overall system robustness It would be appropriate to introduce the aforementioned

methods in [16] [17] and [26] in the ADT part of the pipeline

1httpswww2021ieeeicassporg


Also in terms of classes in the drumset there is a large path to cover in this way

There are no solutions that transcribe in a robust way a whole set including the toms

and different kinds of cymbals In this way we think that a proper approach would

be to work with professional musicians which helps researchers to better understand

the instrument and create datasets with different techniques

In respect of the assessment step apart from the feedback visualization of the tempo

deviations and the reading accuracy a regression model could be trained with drums

exercises assessed and give a mark to each student In this path introducing an

electronic drumset with MIDI output would make things a lot easier as the drums

classifier step would be omitted

About the implementation a good contribution would be to introduce the models

and algorithms to the Pysimmusic workflow and develop a demo web app like the

MusicCriticrsquos But better results and more robustness are needed to do this step

63 Work reproducibility

In computational sciences a work is reproducible if code and data are available and

other researchersstudents can execute them getting the same results

All the code has been developed in Python a widely known general-purpose pro-

gramming language It is available in my GitHub repository2 as well as the data

used to test the system and the classification models

The data created ie the studio recordings are available in a Zenodo repository3

and some samples in Freesound4 This is the 29kDrumsSamplesDataset as not all

the 40k samples used to train are of our property and we are not able to share them

under our full authorship despite this the other datasets used in this project are

available individually

2httpsgithubcomMaciACtfg_DrumsAssessment3httpszenodoorgrecord4923588YMRgNm4p7ow4httpsfreesoundorgpeopleMaciaACpacks32397


64 Conclusions

This project has been developed over one year At this point with the work de-

scribed the goal of supporting drums learning has been accomplished Besides this

work rests in terms of robustness and reliability But a first approximation has been

presented as well as several paths of improvement proposed

Moreover some fields of engineering and computer science have been covered such

as signal processing music information retrieval and machine learning Not only

in terms of implementation but investigating for methods and gathering already

existing experiments and results

About my relationship with computers I have improved my fluency with git and

its web version GitHub Also at the beginning of the project I wanted to execute

everything on my local computer having to install and compile libraries that were

not able to install in macOS via the pip command (ie Essentia) which has been

a tough path to take and accomplish In a more advanced phase of the project

I realized that the LilyPond tools were not possible to install and use fluently in

my local machine so I have moved all the code to my Google Drive to execute the

notebook on a Collaboratory machine Developing code in this environment has

also its clues which I have had to learn In summary I have spent a bunch of time

looking for the ideal way to develop the project and the process indeed has been

fruitful in terms of knowledge gained

In my personal opinion developing this project has been a nice way to close my

Bachelorrsquos degree as I reviewed some of the concepts of more personal interest

And being able to relate the project with music and drums helped me to keep

my motivation and focus I am quite satisfied with the feedback visualization that

results of the system and I hope that more people get interested in this field of

research to get better tools in the future

List of Figures

1 Datasets pre-processing 15

2 Sample drums score from music school drums grade 1 17

3 Microphone setup for drums recording 19

4 Number of samples before Train set recording 20

5 Number of samples after Train set recording 21

6 Proposed pipeline for a drums performance assessment system in-

spired by [2] 25

7 Confusion matrix after training with the dataset in Figure 4 28

8 Confusion matrix after training with the dataset in Figure 5 and

parameter C = 1 29


parameter C = 10 30


parameter C = 10 but only hh sd and kd classes 30

11 Onsets detected in a 60bpm drums interpretation 32


13 Onset deviation plot of a good tempo submission 34

14 Onset deviation plot of a bad tempo submission 34

15 Example of coloured notes 35

16 Good reading and good tempo Ex 1 60 bpm 37





51

52 LIST OF FIGURES



23 Good reading and good tempo Ex 1 60 bpm accumulating +6dB at

each new staff 41

24 Good reading and good tempo Ex 1 220 bpm accumulating +6dB

at each new staff 42

25 Bad reading and bad tempo Ex 1 100 bpm 43


27 Bad reading and good tempo Ex 1 starts on 60 bpm and adds 60bpm


28 Bad reading and good tempo Ex 2 60 bpm 45


30 Good reading and bad tempo Ex 1 100 bpm 46

31 Recording routine 1 57



34 Drumset configuration 1 58














List of Tables

1 Abbreviationsrsquo legend 4

2 Microphones used 19

3 Results of exercise 1 with different tempos 38


5 Results of exercise 1 at 60 bpm with different amplification levels 40


7 Assessment result of a bad reading with different tempos 44 exercise 46


53

Bibliography

[1] Wu C-W et al A review of automatic drum transcription IEEEACM Trans

Audio Speech and Lang Proc 26 (2018)

[2] Eremenko V Morsi A Narang J amp Serra X Performance assessment

technologies for the support of musical instrument learning e-repository UPF

(2020)

[3] MusicTechnologyGroup Pysimmusic httpsgithubcomMTGpysimmusic

[private] (2019)

[4] Kernan T J Drum set [drum kit trap set] Grove Encyclopedy of Music

(2013)

[5] Wachsmann K J Kartomi M von Hornbostel E M amp Sachs C Instru-

ments classification of Grove Encyclopedy of Music (2001)

[6] Mierswa I amp Morik K Automatic feature extraction for classifyng audio data

Mach Learn 58 (2005)

[7] Vos J amp Rasch R The perceptual onset of musical tones Perception Psy-

chophysics 29 (1981)

[8] Bello J P et al A tutorial on onset detection in music signals IEEE Trans-

actions on Speech and Audio Processing (2005)

[9] Essentia Algorithm reference Onsetdetection httpsessentiaupfedu

referencestreaming_OnsetDetectionhtml (2021)

54

BIBLIOGRAPHY 55

[10] Herrera P Peeters G amp Dubnov S Automatic classification of musical

instrument sound Journal of New Music Research 32 (2010)

[11] Schedl M Goacutemez E amp Urbano J Music information retrieval Recent de-

velopments and applications Foundations and Trends in Information Retrieval

8 (2014)

[12] A van Dyk D amp Meng X-L The art of data augmentation Journal of Com-

putational and Graphical Statistics - J COMPUT GRAPH STAT 10 (2012)

[13] Nanni L Maguoloa G amp Paci M Data augmentation approaches for im-

proving animal audio classification CoRR (2020)

[14] Kol T Peddinti V Povey D amp Khudanpur S Audio augmentation for

speech recognition INTERSPEECH (2020)

[15] Adavanne S M Fayek H amp Tourbabin V Sound event classification and

detection with weakly labeled data DCASE 2019 (2019)

[16] Southall C Stables R amp Hockman J Automatic drum transcription for

polyphonicrecordings using soft attention mechanisms andconvolutional neural

networks ISMIR (2017)

[17] Lindsay-Smith H McDonald S amp Sandler M Drumkit transcription via

convolutive nmf 15th International Conference on Digital Audio Effects DAFx

2012 Proceedings (2012)

[18] Miron M EP Davies M amp Gouyon F An open-source drum transcription

system for pure data and max msp 2013 IEEE International Conference on

Acoustics Speech and Signal Processing (2012)

[19] Dittmar C amp Gaumlrtner D Real-time transcription and separation of drum

recordings based onnmf decomposition DAFx (2014)

[20] Southall C Wu C-W Lerch A amp Hockman J Mdb drums ndash an annotated

subset of medleydb forautomatic drum transcription ISMIR (2017)

56 BIBLIOGRAPHY

[21] Gillet O amp Richard G Enst-drums an extensive audio-visual database for

drum signals processing ISMIR (2006)

[22] Marxer R amp Janer J Study of regularizations and constraints in nmf-based

drums monaural separation DAFx (2013)

[23] Bogdanov D et al Essentia An audio analysis library for musicinformation

retrieval Proceedings - 14th International Society for Music Information Re-

trieval Conference (2010)

[24] Goacutemez E Harte C Sandler M amp Abdallah S Symbolic representation of

musical chords A proposed syntax for text annotations ISMIR (2005)

[25] Upton G amp Cook I Laws of large numbers A dictionary of statistics (2008)

[26] Wei I-C Wu C-W amp Su L Improving automatic drum transcription using

large-scale audio-to-midi aligned data ICASSP 2021 - 2021 IEEE International

Conference on Acoustics Speech and Signal Processing (ICASSP) (2021)

Appendix A

Studio recording media

pound poundpound pound pound pound pound pound = 60

pound

poundpound poundpoundpound9 pound poundpound

poundpound poundpoundpound13pound poundpound

pound pound poundpound pound pound pound pound pound 17 pound pound pound pound poundpound pound


Music engraving by LilyPond 2182mdashwwwlilypondorg

Figure 31 Recording routine 1

57

58 Appendix A Studio recording media

frac34frac34frac34frac34pound = 60 frac34frac34

frac34frac34 frac34frac345 frac34frac34

frac34frac34frac34frac34 frac34frac349



poundpoundpound poundpoundpound = 60 pound poundpound

pound pound poundpound pound pound pound poundpound pound pound pound pound poundpound5

pound

poundpoundpound poundpoundpound pound pound pound pound poundpoundpound poundpoundpoundpound pound poundpoundpound 9 pound poundpoundpound pound pound pound poundpound pound pound



Figure 34 Drumset configuration 1

59



Appendix B

Extra results



60

61




62 Appendix B Extra results




63





Introduction

Motivation

Existing solutions

Identified challenges

Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation

Sound event classification

Drums event classification

Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition

Drums event classifier

Feature extraction

Training and validating

Testing

Music performance assessment

Visualization

Files used

Results

Tempo limitations

Saturation limitations

Evaluation of the assessment


Discussion of results

Further work

Work reproducibility

Conclusions

List of Figures

List of Tables

Bibliography


Extra results

Bachelorrsquos Degree on Audiovisuals Systems EngineeringUniversitat Pompeu Fabra

Automatic assessment technologies tosupport drums practicing

Maciagrave Amoroacutes i Cortiella

Supervisor Xavier Serra

May 2021

Contents

1 Introduction 1

11 Motivation 1






14 Objectives 5










241 Essentia 12

242 Scikit-learn 12

243 Lilypond 12

244 Pysimmusic 13

245 Music Critic 13

25 Summary 13



311 MDB Drums 15

312 IDMT Drums 15


321 Music school 16




35 Summary 23

4 Methodology 24





423 Testing 31



432 Files used 35

5 Results 36






62 Further work 48


64 Conclusions 50

List of Figures 51

List of Tables 53

Bibliography 54


B Extra results 60

Acknowledgement




have needed it


Abstract




















Chapter 1

Introduction









11 Motivation









the performances

1





















is not implemented












131 Guitar vs drums






















classification





















133 Signal quality







14 Objectives 5

14 Objectives



the pipeline








15 Project overview












Chapter 2

State of the art













Onset detection




6



















Timbre features










in section 211




spectral flux ia









































Methods














Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

Contents

1 Introduction 1

11 Motivation 1






14 Objectives 5










241 Essentia 12

242 Scikit-learn 12

243 Lilypond 12

244 Pysimmusic 13

245 Music Critic 13

25 Summary 13



311 MDB Drums 15

312 IDMT Drums 15


321 Music school 16




35 Summary 23

4 Methodology 24





423 Testing 31



432 Files used 35

5 Results 36






62 Further work 48


64 Conclusions 50

List of Figures 51

List of Tables 53

Bibliography 54


B Extra results 60

Acknowledgement




have needed it


Abstract




















Chapter 1

Introduction









11 Motivation









the performances

1





















is not implemented












131 Guitar vs drums






















classification





















133 Signal quality







14 Objectives 5

14 Objectives



the pipeline








15 Project overview












Chapter 2

State of the art













Onset detection




6



















Timbre features










in section 211




spectral flux ia









































Methods














Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

241 Essentia 12

242 Scikit-learn 12

243 Lilypond 12

244 Pysimmusic 13

245 Music Critic 13

25 Summary 13



311 MDB Drums 15

312 IDMT Drums 15


321 Music school 16




35 Summary 23

4 Methodology 24





423 Testing 31



432 Files used 35

5 Results 36






62 Further work 48


64 Conclusions 50

List of Figures 51

List of Tables 53

Bibliography 54


B Extra results 60

Acknowledgement




have needed it


Abstract




















Chapter 1

Introduction









11 Motivation









the performances

1





















is not implemented












131 Guitar vs drums






















classification





















133 Signal quality







14 Objectives 5

14 Objectives



the pipeline








15 Project overview












Chapter 2

State of the art













Onset detection




6



















Timbre features










in section 211




spectral flux ia









































Methods














Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results






62 Further work 48


64 Conclusions 50

List of Figures 51

List of Tables 53

Bibliography 54


B Extra results 60

Acknowledgement




have needed it


Abstract




















Chapter 1

Introduction









11 Motivation









the performances

1





















is not implemented












131 Guitar vs drums






















classification





















133 Signal quality







14 Objectives 5

14 Objectives



the pipeline








15 Project overview












Chapter 2

State of the art













Onset detection




6



















Timbre features










in section 211




spectral flux ia









































Methods














Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

Acknowledgement




have needed it


Abstract




















Chapter 1

Introduction









11 Motivation









the performances

1





















is not implemented












131 Guitar vs drums






















classification





















133 Signal quality







14 Objectives 5

14 Objectives



the pipeline








15 Project overview












Chapter 2

State of the art













Onset detection




6



















Timbre features










in section 211




spectral flux ia









































Methods














Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

Abstract




















Chapter 1

Introduction









11 Motivation









the performances

1





















is not implemented












131 Guitar vs drums






















classification





















133 Signal quality







14 Objectives 5

14 Objectives



the pipeline








15 Project overview












Chapter 2

State of the art













Onset detection




6



















Timbre features










in section 211




spectral flux ia









































Methods














Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

Chapter 1

Introduction









11 Motivation









the performances

1





















is not implemented












131 Guitar vs drums






















classification





















133 Signal quality







14 Objectives 5

14 Objectives



the pipeline








15 Project overview












Chapter 2

State of the art













Onset detection




6



















Timbre features










in section 211




spectral flux ia









































Methods














Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results





















is not implemented












131 Guitar vs drums






















classification





















133 Signal quality







14 Objectives 5

14 Objectives



the pipeline








15 Project overview












Chapter 2

State of the art













Onset detection




6



















Timbre features










in section 211




spectral flux ia









































Methods














Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results







131 Guitar vs drums






















classification





















133 Signal quality







14 Objectives 5

14 Objectives



the pipeline








15 Project overview












Chapter 2

State of the art













Onset detection




6



















Timbre features










in section 211




spectral flux ia









































Methods














Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results





















133 Signal quality







14 Objectives 5

14 Objectives



the pipeline








15 Project overview












Chapter 2

State of the art













Onset detection




6



















Timbre features










in section 211




spectral flux ia









































Methods














Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

14 Objectives 5

14 Objectives



the pipeline








15 Project overview












Chapter 2

State of the art













Onset detection




6



















Timbre features










in section 211




spectral flux ia









































Methods














Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

Chapter 2

State of the art













Onset detection




6



















Timbre features










in section 211




spectral flux ia









































Methods














Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results



















Timbre features










in section 211




spectral flux ia









































Methods














Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results




spectral flux ia









































Methods














Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results



















Methods














Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results







Datasets




described













download11














music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results











music sheet





reviewed

24 Software tools










241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results


241 Essentia







242 Scikit-learn






243 Lilypond










25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

25 Summary 13

244 Pysimmusic






reference

245 Music Critic







25 Summary





development



Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

Chapter 3










modification









14




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results




audio + txt



Write annotations

AnnotationsAudio


311 MDB Drums









ghost note)

312 IDMT Drums







32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results




32 Created datasets








321 Music school










MusicXML parser




























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results























notes


















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results















skill levels



















Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results







Train set





















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results















itself is intrinsic











0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results








0

1000

2000

3000

ht+kd

kd+m

t

ht

mt

ft+sd

ft+kd

+sd

cr+sd

ft

cr+kd

cr

ft+kd

hh+k

d+sd

kd+s

d

cy+s

d

cy

cy+k

d sd

kd

hh+s

d

hh+k

d

hh



Test set





































35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results





























35 Summary









crophones



Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

Chapter 4

Methodology





process













24


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results


Assessments

Music Scores


Annotations

Audio recordings

Dataset

Feature extraction



training

Feature extraction


inference







































features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

























features



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results



















accuracy(y y) =1

nsamples


1(yi = yi) (41)





not a good result

wi =wisum

j 1(yj = yi)wj

(42)


sumi

1(yi = yi)wi









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results









(43)




















shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results














shown in Figure 10






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results






















423 Testing











scriptsdrumspyL45




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results




HFC(n) =sumk



























the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results
















the proposed ones



expected ones

431 Visualization














































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results







































432 Files used









Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

Chapter 5

Results











results





36























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results























































































onset


Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

Chapter 6



















47


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results


















62 Further work







































64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results




























64 Conclusions


























List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

List of Figures







spired by [2] 25



parameter C = 1 29


parameter C = 10 30













51

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

52 LIST OF FIGURES




each new staff 41



























List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

List of Tables









53

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

Bibliography





(2020)


[private] (2019)


(2013)











54

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

BIBLIOGRAPHY 55





8 (2014)






















56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

56 BIBLIOGRAPHY














Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

Appendix A



pound







57









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results









pound





59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

59



Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

Appendix B

Extra results



60

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

61








63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results





63





Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results



Introduction

Motivation

Existing solutions


Guitar vs drums

Dataset creation

Signal quality

Objectives

Project overview

State of the art

Signal processing

Feature extraction

Data augmentation



Digital sheet music

Software tools

Essentia

Scikit-learn

Lilypond

Pysimmusic

Music Critic

Summary


Existing datasets

MDB Drums

IDMT Drums

Created datasets

Music school

Studio recordings

Data augmentation

Drums events trim

Summary

Methodology

Problem definition


Feature extraction


Testing


Visualization

Files used

Results

Tempo limitations





Further work


Conclusions

List of Figures

List of Tables

Bibliography


Extra results

automatic assessment technologies to support drums practicing

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