Chord Recognition

EE6820 Speech and Audio Signal Processing and Recognition

Mid-term Presentation

JunHao Ip

Chord Recognition:

Introduction and Background Information

Previous methods

Feature description

Experimental procedures

Introduction:

Music transcription >> Very difficult task >> Requires strong musical background and training

Chord transcription >> Some musical background >> Good ears >> Time consuming

Needed for automation >> Very challenging problem >> Limited successes

Investigate existing methods and try new techniques for Chord recognition

Musical Background:

Chord is a several tones played simultaneously.

It is usually played in a group of three tones called Triad.

Chord symbol is defined by root note and the key associated with it.

Chord Families

maj, min, maj7, min7, dom7, aug, dim

Roots Ab,Bb,Cb,Eb,Fb,Gb

A,B,C,D,E,F,G

A#,B#,C#,D#,E#,F#,G#

Previous Methods:

Manual transcription >> Currently the most accurate technique of all >> Strong musical training is needed >> Very time consuming

EM Trained Hidden Markov Model >> Compare performance of MFCC and Pitch Class Profile (PCP) >> PCP outperforms MFCC >> 83.3% accuracy in chord alignment, 26.4% accuracy in recognition

Chord Progression Hypothesis Model >> Attempt to make educational guess on chord progression >> Find keys, chord symbols, and beats concurrently >> Uses Chroma Vectors for chord estimation, similar to PCP >> 77% accuracy in recognition

Pitch Class Profile:

Combine pitches from different octaves to form a 12 semi-tones vector from (Ab to G#)

Feature is popular

Problem with data presentation when voice and multi instruments present.

Use 24 bins instead of 12

p [k] = floor( 24 log2( (k / N) (fs / fref) ) mod 24

PCP [p] = sum ( |X[k]|2 )

k = frequency index p = bin index

Fs = sampling frequency Fref = 440Hz (note A)

Use image processing technique?

Autocorrelation of Sub-band energy envelope:

New technique

Investigate correlation of sub-band energy envelope with long lag

The least common multiple of the different fundamental frequencies making up a chord

Rxx[l] = ∑ x[n] x[n-l] = Rxx[-l]

Experiments:

Investigate the PCP feature

Sub-band Autocorrelation feature

Feature comparison

Train by Expectation Maximization Model? Nearest Neighbor?

Measure accuracy of Chord classification using test sets

Reference:

1. Chord Segmentation and Recognition using EM – Trained Hidden Markov Model, Alex Sheh and Daniel P.W. Ellis, 2003

2. Automatic Chord Transcription with Concurrent Recognition of Chord Symbols and Boundaries, Takuya Yoskioka, Tetsuro Kitahara, Kazunori Komatani, Tetsuya Ogata, and Hiroshi G. Okuno, 2004

3. A Chorus-section Detecting Method for Musical Audio Signals

4. Transcription Techniques – part 1, Lucas Pickfordhttp://www.globalbass.com/archives/dec2000/transcription_techniques.htm

5. Introductory Musicianship A Workbook 6th Ed, Thomson Schirmer Inc, Theodore A. Lynn

6. Speech and Audio Signal Processing, John Wiley & Sons Inc, Ben Gold and Nelson Morgan