Columbia ELEN E4896 - Chroma and Chords (19 pages)

Previewing pages 1, 2, 3, 4, 5, 6 of 19 page document View the full content.
View Full Document

Chroma and Chords



Previewing pages 1, 2, 3, 4, 5, 6 of actual document.

View the full content.
View Full Document
View Full Document

Chroma and Chords

52 views


Pages:
19
School:
Columbia University
Course:
Elen E4896 - MUSIC SIGNAL PROCESSING
Unformatted text preview:

ELEN E4896 MUSIC SIGNAL PROCESSING Lecture 11 Chroma and Chords 1 Features for Music Audio 2 Chroma Features 3 Chord Recognition Dan Ellis Dept Electrical Engineering Columbia University dpwe ee columbia edu E4896 Music Signal Processing Dan Ellis http www ee columbia edu dpwe e4896 2013 04 08 1 18 1 Features for Music Audio Challenges of large music databases how to find what we want Euclidean metaphor music tracks as points in space What are the dimensions sound timbre instruments MFCC melody chords Chroma rhythm tempo Rhythmic bases E4896 Music Signal Processing Dan Ellis 2013 04 08 2 18 MFCCs Logan 2000 The standard feature for speech recognition 0 5 Sound 0 FFT X k spectra 0 5 0 25 0 255 0 26 0 265 0 27 time s 10 Mel scale freq warp log X k audspec 5 0 4 0x 10 1000 2000 3000 freq Hz 0 5 10 15 freq Mel 0 5 10 15 freq Mel 0 10 20 30 quefrency 2 1 0 100 50 IFFT cepstra 0 200 0 Truncate 200 MFCCs E4896 Music Signal Processing Dan Ellis 2013 04 08 3 18 MFCC Example Resynthesize by imposing spectrum on noise MFCCs capture instruments not notes freq Hz Let It Be log freq specgram LIB 1 6000 1400 300 coefficient MFCCs 12 10 8 6 4 2 freq Hz Noise excited MFCC resynthesis LIB 2 6000 1400 300 0 5 E4896 Music Signal Processing Dan Ellis 10 15 20 25 time sec 2013 04 08 4 18 MFCC Artist Classification Ellis 2007 20 Artists x 6 albums each train models on 5 albums classify tracks from last Model as MFCC mean covariance per artist u2 tori amos suzanne vega steely dan roxette radiohead queen prince metallica madonna led zeppelin green day garth brooks fleetwood mac depeche mode dave matthews b cure creedence c r beatles aerosmith u2 to su st ro ra qu pr me le ma gr fl ga de da cu cr be ae E4896 Music Signal Processing Dan Ellis Confusion MFCCs acc 55 13 true single Gaussian model 20 mean 10 x 19 covariance parameters 55 correct guessing 5 2013 04 08 5 18 2 Chroma Features MIDI note number What about modeling tonal content notes 75 melody spotting chord recognition cover songs 70 65 60 55 MFCCs exclude tonal content Polyphonic transcription is too hard 45 40 MIDI note number 50 e g sinusoidal tracking confused by harmonics 75 70 65 60 55 50 Recognized True 40 45 22 24 26 Chroma features as solution E4896 Music Signal Processing Dan Ellis 28 30 32 34 2013 04 08 6 18 Chroma Features Fujishima 1999 Idea Project all energy onto 12 semitones regardless of octave maintains main musical distinction invariant to musical equivalence no need to worry about harmonics G F 3 2 D C A chroma freq kHz chroma 4 G F D C 1 50 100 150 fft bin 0 2 4 6 8 time sec A 50 100 150 200 250 time frame NM C b B 12 log2 k k0 b W k X k k 0 W k is weighting B b selects every mod12 E4896 Music Signal Processing Dan Ellis 2013 04 08 7 18 Better Chroma Problems blurring of bins close to edges limitation of FFT bin resolution Solutions D C A 4 freq kHz chroma G F 0 2000 freq Hz 3 2 1 0 2 4 6 8 time sec chroma peak picking only keep energy at center of peaks G F D C A 50 100 150 200 time frame Instantaneous Frequency high resolution estimates adapt tuning center based on histogram of pitches E4896 Music Signal Processing Dan Ellis 2013 04 08 8 18 Chroma Resynthesis Ellis Poliner 2007 Chroma describes the notes in an octave but not the octave Can resynthesize by presenting all octaves with a smooth envelope Shepard tones octave is ambiguous M 0 10 20 30 40 50 60 12 Shepard tone spectra freq kHz level dB b b o 12 yb t W o cos 2 w0 t 12 o 1 Shepard tone resynth 4 3 2 1 0 500 1000 1500 2000 2500 freq Hz 0 2 4 6 8 10 time sec endless sequence illusion E4896 Music Signal Processing Dan Ellis 2013 04 08 9 18 Chroma Example Simple Shepard tone resynthesis can also reimpose broad spectrum from MFCCs freq Hz Let It Be log freq specgram LIB 1 6000 1400 300 chroma bin Chroma features B A G E D C freq Hz Shepard tone resynthesis of chroma LIB 3 6000 1400 300 freq Hz MFCC filtered shepard tones LIB 4 6000 1400 300 0 5 E4896 Music Signal Processing Dan Ellis 10 15 20 25 time sec 2013 04 08 10 18 Beat Synchronous Chroma Bartsch Wakefield 2001 Drastically reduce data size by recording one chroma frame per beat Let It Be log freq specgram LIB 1 freq Hz 6000 1400 300 chroma bin Onset envelope beat times Beat synchronous chroma B A G E D C Beat synchronous chroma Shepard resynthesis LIB 6 freq Hz 6000 1400 300 0 5 E4896 Music Signal Processing Dan Ellis 10 15 20 25 time sec 2013 04 08 11 18 3 Chord Recognition G 5 A C D F 0 A C E A B D G E D C C E G chroma bin Beat synchronous chroma look like chords 10 15 20 time sec can we transcribe them Two approaches manual templates prior knowledge learned models from training data E4896 Music Signal Processing Dan Ellis 2013 04 08 12 18 Chord Recognition System Analogous to speech recognition Sheh Ellis 2003 Gaussian models of features for each chord Hidden Markov Models for chord transitions Beat track Audio test 100 1600 Hz BPF Chroma 25 400 Hz BPF Chroma beat synchronous chroma features C maj B A G train Labels chord labels HMM Viterbi Root normalize Gaussian Unnormalize 24 Gauss models E D C C D E G A B C D E G A B c min B A G E D Resample C b a g Count transitions 24x24 transition matrix f e d c B A G F E D C E4896 Music Signal Processing Dan Ellis C D EF G A B c d e f g a b 2013 04 08 13 18 HMMs Hidden Markov Models are good for inferring hidden states 8 8 S underlying Markov generative model each state has emission distribution 1 1 A 1 B 1 1 C p qn 1 qn S A qn B C E 1 1 7 E qn 1 S A B C E 0 0 0 0 0 1 8 1 1 0 0 1 8 1 0 0 1 1 7 0 0 0 0 1 1 SAAAAAAAABBBBBBBBBCCCCBBBBBBCE E4896 Music Signal Processing Dan Ellis q B q C 3 0 6 xn p x q q A 0 8 AAAAAAAABBBBBBBBBBBCCCCBBBBBBBC 0 4 0 2 0 0 8 q A q B q C 3 0 6 0 4 2 1 0 2 0 Observation sequence 1 0 2 xn infer smoothed state sequence State sequence …


View Full Document

Access the best Study Guides, Lecture Notes and Practice Exams

Loading Unlocking...
Login

Join to view Chroma and Chords and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Chroma and Chords and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?