Music Data MiningName :- Kaushik SamantRoll no :- 05Course :- Msc(I.T)Part 1

DATA MINING

The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.

Data mining uses information from past data to analyze the outcome of a particular problem or situation that may arise. Data mining works to analyze data stored in data warehouses that are used to store that data that is being analyzed.

The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records unusual records and dependencies.

MUSIC DATA MINING

Music plays an important role in the everyday life for many people, and with the digitalization, large music data collections are formed and tend to be accumulated further by music enthusiasts.

This has lead to music collections -not on the shelf in form of audio or video records and CD’s - but on the hard drive and on the internet, to grow beyond what previously was physical possible.

It has become impossible for humans to keep track of music and the

relations between songs and pieces, and this fact naturally calls for data mining and machine learning techniques to assist in the navigation within the music world . The objective of this presentation is to give you

overview of some music data mining tasks and navigate various data mining approaches with respect to such tasks.

Different music bands and artists

In case of a music data collection, the data sources can be many things such as, it can be the music itself, or metadata such as the title of the songs, or some acoustic features underlying audio pieces,or even statistics of how many people have listened to a track.

On What Kind of Tasks?

Music data mining involves methods for various tasks, e.g., genre classification,artist/singer identification, mode/motion , detection of instrument recognition,music similarity search, music summarization and visualization, and so forth.

Different music data mining tasks focus on different data sources, and try to explore different aspects of data sources.

On What Kind of Data?

Music Data Mining Task

Music Similarity Search In the field of music data mining, similarity search

refers to searching for music sound files similar to a given music sound file.

In principle, searching can be carried out on any dimension.

The similarity search processes can be divided into feature extraction and query the main task is to employ a proper similarity measure to calculate the similarity between the given music work and the candidate music work system processing.

The objective of similarity search is to find music sound files similar to a music sound file given as input.

Music classification based on genre and style is naturally the form of a hierarchy.

Similarity can be used to group sounds together at any node in the hierarchies. The use of sound signals for similarity is justified by an observation that audio signals (digital or analog) of music belonging to the same genre share certain characteristic ,because they are composed of similar types of instruments, having similar rhythmic patterns, and similar pitch distributions.

Clustering

Clustering is a technique where data are divided into different clusters .

Cluster are small different sets of data which have a same behavior within in a group.

Clustering is the task of separating a collection of data into different groups based on some criteria.

Specifically in music data mining, clustering aims at dividing a collection of music data into groups of similar objects based on their pairwise similarities without predefined class labels.

It has been shown that the characteristics of a singer’s voice can be extracted

from music via vocal segment detection followed by solo vocal signal modeling which propose a clustering algorithm that integrates features from both lyrics and acoustic data sources to perform bimodal learning

Music Sequence Mining

Sequence mining is the task to find patterns which are presented in a certain number of data instances. The instances consist of sequences of elements.

The detected patterns are expressed in terms of sub-sequences of the data sequences and impose an order, i.e., the order of the elements of the pattern should be respected in all instances where it appears.

The pattern is considered to be frequent if it appears in a number of instances above a given threshold value ,usually defined by the user.

These patterns may represent valuable information.

For ex– maximum number of passionate fans.

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