Chapter 4: Pitch estimation for music signal processing

KH Wong

Ch4. pitch, v3.c 1

Introduction (lecture 4)

• Pitch estimation is essential to many music signal applications– Genre classification– Music tutor: detection of playing fault– Music style analysis– Automatic transcription, audio signal music

score

Ch4. pitch, v3.c 2

Techniques in pitch extraction

– Time domain approaches• (1) ACF (Autocorrelation function) and MACF (Modified

Autocorrelation function)• (2) Normalized cross correlation function NCCF • (3) AMDF (Average magnitude difference function)

– Frequency domain approaches• (4) Cepstrum Pitch Determination (CPD)

Ch4. pitch, v3.c 3

Definition of pitch

• What is the pitch (音高 ) of a tone?• Answer: The perceived frequency of sound.

(wiki)

Ch4. pitch, v3.c 4

Method 1:ACF (Autocorrelation function)

• Autocorrelation function (ACF)

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Ch4. pitch, v3.c 5

Symmetrical on both sideR

x

n

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m

What is Auto-correlation, R(m)?• E.g.• x=[1 5 7 1 4 ]• N=5, • R(0)=[x(0)*x(0)+x(1)*x(1)+x(2)*x2+x(3)*x(3)+x(4)*x(4)]• R(0)= (1+ 25+49+1+16)=92

• R(1)=[x(0)*x(1)+x(1)*x(2)+x(2)*x(3)+x(3)*x(4)] • x=[1 5 7 1 4 ] • [1 5 7 1 4 ]• (5+ 35+ 7+ 4)=51• And so on…• R=[92.0000 51.0000 40.0000 21.0000 4.0000]•

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Ch4. pitch, v3.c 6

Exercise 4.1First, what is auto-correlation?

• %matlab code• x=[1 5 7 1 4 8 6 2 4 9 3 ]'• auto_corr_x=xcorr(x) %auto-

correlation• figure(1), clf• subplot(2,1,1),plot(x)• grid on, grid(gca,'minor'), hold on• subplot(2,1,2),plot(auto_corr_x)• grid on, grid(gca,'minor')

• Exercise:• Show the steps of calculation

•

Ch4. pitch, v3.c 7

X[t]

Auto_correlation(x[t])t

•We only look at positive n•Gap between two peaks is 4, so period of X is around 4

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Ans: ??

autocorrelation• When a segment of a signal is correlated with itself, the distance (-

=Lag_time_in_samples) between the positions of the maximum and the second maximum correlation is defined as the fundamental period (pitch) of the signal.

Ch4. pitch, v3.c 8

Lag Time jin samples

Auto correlation R(j)

Rthe_max (j1)Rsecond_max (j2)

j1=0 j2

Then the fundamental frequency can be calculated as:

• Then the fundamental frequency can be calculated as:

• Usually =0, because is at .

Ch4. pitch, v3.c 9

12

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Modified Auto-Correlation Method:Auto-Correlation Method enhanced by Center clipping

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result because higher frequency signals will not interfere with the result

Ch4. pitch, v3.c 10

CLCL

Cut(remove) the middle part

X(n)

n

n

y(n) =clc(x)

Typical CL =1/4 peak-to-peak of X

Finding pitchby center clipping

• In R(m) auto correlation of x(n), it is not easy to pick peaks

• In R’(m), auto correlation of clipped signal y(n)=clc{x(n)}, peaks are easy to pick

Ch4. pitch, v3.c 11

T=mean(T1,T2,T3)=Period=1/(pitch_frequency)

T1 T2 T3

R(m)

R’(m)

X(n)

Y(n)=CenterClipped

The MACF (Modified Autocorrelation function) algorithm

•

Ch4. pitch, v3.c 12

Example

• For each frame, find a pitch.

• Plot pitch against time (blue), you can see the pitch profile

Ch4. pitch, v3.c 13

time

Time n (frame)

X(n)

Pitch (n)frequency

Class exercise 4.2

• x=[1 3 7 2 1 9 3 1 8 ], If Fs= sampling frequency= 1Hz.

• (a) Find pitch of this signal x using ACF (Autocorrelation function) .

• (b) Repeat above of if Fs = 8KHz

Ch4. pitch, v3.c 14

Method 2:Normalized cross correlation function NCCF method

[Verteletskaya 2009 ]

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Ch4. pitch, v3.c 15

Method 3:Average Magnitude Difference Function (AMDF) Method

[Verteletskaya 2009 ]

• An intuitive method, just pick the peaks and find the period

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Ch4. pitch, v3.c 16

Find peaks in D, the estimated period is the average gaps between two neighboring –ve peaks

peaks

Method 4:Cepstrum Pitch Determination (CPD)

[Verteletskaya 2009 ] •

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Ch4. pitch, v3.c 17

The problem : For human voice, the peak may be the result of glottal excitation.

Q’

Peak at Q’, Pitch =1/0.006=166Hz.

For human voice pitch detection (or recognition )

• We must study its structure of the vocal system and find out how to get the accurate answer.

• vocal system has 2 elements– Glottal excitation (no use for pitch measurement)– Vocal tract filter– Use liftering to remove glottal excitation before

we use the spectrum of the vocal tract filter for pitch extraction.

Ch4. pitch, v3.c 18

Cepstrum of speech• A new word by reversing the first 4 letters of spectrum

cepstrum.• It is the spectrum of a spectrum of a signal• Why we need this?

– Answer: remove the ripples – of the spectrum caused by – glottal excitation.

Ch4. pitch, v3.c 19Speech signal x

Spectrum of x

Too many ripples in the spectrum caused by vocalcord vibrations.But we are more interested in the speech envelope for recognition and reproduction

FourierTransform

http://isdl.ee.washington.edu/people/stevenschimmel/sphsc503/files/notes10.pdf

Liftering method: Select the higher and lower samples

•

Ch4. pitch, v3.c 20

Signal X(n)

Cepstrum=C(n)=fft|(log|fft(x(n))|)|

Select high time liftering, select C_high (lower frequency):glottal excitation

Select low time liftering,Select C_low (higher frequency) :Vocal tract filter response

Quefrency is in time domain (in second)So Higher Quefrency lower frequency

Recover Glottal excitation and vocal track spectrum

•

Ch4. pitch, v3.c 21

C_highForGlottalexcitation

C_highForVocal track

This peak may be the pitch period:This smoothed vocal track spectrum can be used to find pitchFor more information see :

http://isdl.ee.washington.edu/people/stevenschimmel/sphsc503/files/notes10.pdf

Frequency

Frequency

quefrency (sample index)

Cepstrum of glottal excitation

Spectrum of glottal excitation

Spectrum of vocal track filterCepstrum of vocal track

Measure pitch of musical instruments Example: Find pitch of Oboe A4 sound

http://www.cse.cuhk.edu.hk/%7Ekhwong/www2/cmsc5707/A4_oboe.wav

• A4_Oboe• Spectrogram

Ch4. pitch, v3.c 22

Example: Find pitch of Oboe A4 sound http://www.cse.cuhk.edu.hk/%7Ekhwong/www2/cmsc5707/A4_oboe.wav

http://www.cse.cuhk.edu.hk/%7Ekhwong/www2/cmsc5707/demo_ceps_note_v3.zip

•

Ch4. pitch, v3.c 23

Input:Oboe A4X(n)

Fourier TransformX(w)=fft(x)

Cepstrum C(n)=fft|(log|fft(x(n))|)|From range 200To 900 Hz

Cepstrum C(n)All range, aroundFrom 30 to Hz

The first peak of the cepstrum (in Quefrency) time=0.002268(1/time)=F1=440.91Hz is the pitch, it has the strongest energy

The second peak: time=0.004535(1/time)=F2=220.507

200Hz1/200=5x10^-3

900Hz1/900=1.11x10^-3

Hz

This axis is in x10^-3

Found two Harmonics 440, 220Hz

Summary

• Methods of pitch extraction have been studied.

• Cepstrum and its use for pitch extraction is discussed.

Ch4. pitch, v3.c 24

References• [Naotoshi Seo 2007] Project: Pitch Detection,

]http://note.sonots.com/SciSoftware/Pitch.html#ke283f3a• [Verteletskaya 2009 ] E. Verteletskaya, B. Šimák,” Performance

Evaluation of Pitch Detection Algorithms”, http://access.feld.cvut.cz/view.php?cisloclanku=2009060001

• [Rabiner1976] Rabiner, L.; Cheng, M.; Rosenberg, A.; McGonegal, C." A comparative performance study of several pitch detection algorithms",IEEE Transactions on Acoustics, Speech and Signal Processing, Volume: 24, Issue:5 page(s): 399 - 418, Oct 1976

Ch4. pitch, v3.c 25

Appendix

Ch4. pitch, v3.c 26

Music Frequency tablehttp://wc.pima.edu/~manelson/MUS%20102/MIDI%20tunings%20per%20note.jpg

•

Ch4. pitch, v3.c 27

Music frequency table% source : http://www.angelfire.com/in2/yala/t4scales.htm

•

Ch4. pitch, v3.c 28

Autocorrelation

• In signal processing, given a signal f(t), the continuous autocorrelation is the continuous cross-correlation of f(t) with itself, at lag τ, and is defined as:

• In discrete system, autocorrelation R at lag j for signal is defined as:

Ch4. pitch, v3.c 29

dttftfdttftfffR f )()()()()()()( ***

n

njnn xxjR ))(()(

Anwer4.1: Exercise 4.1First, what is auto-correlation?

• %matlab code• x=[1 5 7 1 4 8 6 2 4 9 3 ]'• auto_corr_x=xcorr(x) %auto-

correlation• figure(1), clf• subplot(2,1,1),plot(x)• grid on, grid(gca,'minor'), hold on• subplot(2,1,2),plot(auto_corr_x)• grid on, grid(gca,'minor')

• Exercise:• Show the steps of calculation

•

Ch4. pitch, v3.c 30

X[t]

Auto_correlation(x[t])t

•We only look at positive n•Gap between two peaks is 4, so period of X is around 4

mN

n

MmmnxnxmR1

000 ),()()(

Ans: [302 214 142 183 194 116 65 88 70 24 3 0]

Answer 4.2 for exercise 4.2It is using MACF, you can use ACF, and the result for the pitch found is the

same for this example.• Question: x=[1 3 7 2 1 9 3 1 8 ], sampling at 1Hz.Find pitch of this signal x using MACF (Modified

Autocorrelation function) .• %%%%%%%%%%%%%%Answer: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%• orginal_x = 1 3 7 2 1 9 3 1 8• x =centered_wave =orginal_x-mean_x =• -2.8889 -0.8889 3.1111 -1.8889 -2.8889 5.1111 -0.8889 -2.8889 4.1111• cl=center clipped range= 2• y =center clipped signal=• -2.8889 0 3.1111 0 -2.8889 5.1111 0 -2.8889 4.1111• (a) if the sampling frequency Fs = 1KHz• >> Answer: from the autocorrelation result of y in the figure, we can see that the distance between 2

peaks is 3, so pitch is 1/3 Hz, since the sampling is 1 Hz..

Ch4. pitch, v3.c 31

Answer 4.2: Class exercise 4.2 • R=[ 24.3333, 9.6667,

8.2222, 16.3333, 6.5556, 4.5556, ,6.8889, 2.7778, 0.8889]

• 2nd diagram, R(+ve only) , pick 2 peaks, Period is 3, frequency =1/3 hz

• (b) if FS = 8KHz• Answer: If the sampling

frequency is Fs=8KHz, sampling period is dt=1/Fs=(1/8)ms , the period of x is 3 units, therefore the actual time is 3*dt= 3*(1/8)ms. The frequency of x is 1/dt=(8/3) KHz

Ch4. pitch, v3.c 32

Matlab• %Ver2, MACF (Modified Autocorrelation function)using center clipping• clear• %select one of the followings• %real_data=1 %1 or 0• real_data=0• if real_data==1• %use real sound• %[x,fs]=wavread ('d:\0music\sounds\violin3.wav');• [orginal_x,fs]=wavread ('violin3.wav');• x=x(10000:11000);• else• %use test data• %x=[1 2 5 6 7 6 1 0 4 3 4 8 6 7 3 2 4 9 3 ]• orginal_x=[1 3 7 2 1 9 3 1 8 ]• fs=1 %assume frquecy is 1Hz• end• %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% test • x=orginal_x-mean(orginal_x)• n=length(x)• maxx=max(x)• minx=min(x)• dd=maxx-minx• figure(1)• clf• plot(x)• %pause• %center clipping algo for pitch extraction• if real_data==1 • cl=dd/4000• else• cl=dd/4 %center clippped "cl" length is 1/4 of total peak-to_peak span• pause• end

• %assume the signal x is voltage against time• %center clip means set those signals with levels within the clipped

regions• %center = mean voltage level of the whole signal • %positive peak = maxim,um of the signal voltage• %negative peak = minimum of the signal voltage• %center clip regions are:(i) from center to 1/2 of center_to_positive

peak• % (ii) from center to -1/2 from center_to_negative peak• for t=1:n• if x(t)<cl & x(t) > -1*cl %those within center clipped region set to 0• y(t)=0;• else• y(t)=x(t);• end;• end ;• auto_corr_y=xcorr(y) %auto correlation• figure(2)• clf• subplot(3,1,1),plot(x)• ylabel('x=centered wave')• subplot(3,1,2),plot(y)• ylabel('y=center clipped wave')• hold on• subplot(3,1,3),plot(auto_corr_y)• ylabel('auto correlation of y')• xlabel('time ')• max_list=max(y)• fs• 'orginal_x ' , orginal_x• 'x =centered_wave =orginal_x-mean_x ' , x• 'cl=center clipped range', cl• 'y =center clipped signal' , y

Ch4. pitch, v3.c 33