Pitch Change Demonstration Code

This m-file demonstrates how to use the Pitch Change Toolkit to calculate how the pitch of a signal changes. Often a single value for the pitch is hard to calculate due to octave ambiguities and other effects at the onset and offset of voicing. But the change in pitch is usually what we care about, and this is easier to calculate.

Preliminaries
Read audio and model for testing.
Calculate the necessary correlograms
Estimate the pitch probabilities of the test sound
Estimate the pitch (Hz) at each time and plot the results.
Pitch Change
Compute a new pitch model
Measure new pitch model's performance

Preliminaries

We need both the Auditory Toolbox and Nabney's Netlab. Make sure we have the tools we need on our path. Change these directory names to suit your own installation.

if ~exist('ERBFilterBank', 'file')
	path(path, '../../AuditoryToolbox');    % Add the Auditory Toolbox
end

if ~exist('mlpfwd', 'file')
    path(path, '../../Netlab');             % Add Netlab for machine learning
end
colormap('default');

Read audio and model for testing.

Lets first test an exisiting pitch model. Read in an audio file and the pitch model

load grazPitchModel.mat

[inputSound,inputFs] = audioread('testSpeech.wav');

Calculate the necessary correlograms

Next we calculate the correlograms of the sound. This requires a cochlear filter bank (gammatone) and the auto-correlation calculation. The correlogram image is unwound and is stored as one column in the correlogramData file.

We can then display one frame of the correlogram (the one frame with the most energy, which is voiced speech.)

frameRate = 100;
frameWidth = 512;
[correlogramData, frameRate, numChannels] = PitchCorrelogram(inputSound, inputFs, frameRate, frameWidth);

if 1
    frameEnergy = sum(correlogramData.^2);
    [m,i] = max(frameEnergy);           % Find the most energetic frame for plotting
    clf; imagesc(reshape(correlogramData(:,i), [], frameWidth));
    xlabel('Autocorrelation Delay (tau in samples)');
    ylabel('Cochlear Channel');
    title('A Correlogram Frame');
end

Estimate the pitch probabilities of the test sound

Given the correlogram, use PCA to reduce its dimensionality, and then use an MLP (multi-layer perceptron) to estimate the pitch-class probability at each time.

pcaPitch = PCATransform(correlogramData, pitchModel.pcaModel);

quantizedPitchEvidence = mlpfwd(pitchModel.mlpModel, pcaPitch')';

% The output is an estimate of the pitch class, as a function of time
% (frame number, 100 frames/second).
imagesc(quantizedPitchEvidence);
xlabel('Frame Number'); ylabel('Pitch Class'); title('Pitch Probabilities');
axis xy
% Pitch Class 1 (row 1 in the image below) is used to indicate unvoiced.

Estimate the pitch (Hz) at each time and plot the results.

Use a Viterbi search to find the optimum tradeoff between observations, prior pitch probabilities, and the pitch-class transition probabilities.

fileStruct = struct('mlpCorrelogram', quantizedPitchEvidence);
[viterbiPitch,statePath,pObservations,pTransitions] = ...
    CalculateViterbiPitch(fileStruct, pitchModel);

Pitch Change

Now estimate the pitch-change signal (by correlating neighboring pitch-probability estimates) and plot the results. The pitch model estimates the likelihood of each possible pitch. We correlate this vector (one dimension per pitch class) over time to get the pitch-change signal. In the plot below, the gray-scale image shows the pitch-change probability signal (computed by correlating over time). While the blue line shows the "exact" position of the peak, computed by fitting the quadratic curve to the peak, and using super-resolution.

smoothWindowLength = 5;
debug = 0;
[deltaPitch, pitchChanges] = PitchChange(quantizedPitchEvidence, smoothWindowLength, debug);

clf; colormap(1-gray);
nT = floor(size(pitchChanges,1));
imagesc(1:size(pitchChanges,2), -nT:nT, pitchChanges);
hold on;
cleanPitchChange = deltaPitch;
cleanPitchChange(isnan(viterbiPitch)) = nan;
plot(1:size(cleanPitchChange,2), cleanPitchChange);
hold off;
xlabel('Frame Number'); ylabel('Pitch Change Signal');
ylim([-6 6])            % Only care about the center of this change signal

Compute a new pitch model

The ComputePitchModel routine does all the necessary work to create a new pitch model. It starts with a database name and does the following: 1) Compute all correlograms 2) Find the best PCA model to approximate each correlogram data 3) Reduces the dimensionality of the correlogram data 4) Find best MLP (multi-layer perceptron) to estimate the pitch class All the results are returned in a pitchModel struct, which stores all the necessary data so we can compute with this model.

The MLP training code splits the data so 75% is used for training and 25% is used for testing. The training/testing results are sorted by their true pitch, so the correct result should be monotonic.

This step takes a while.

pcaThresh = 5;
databaseToUse = 'graz';
% We can't fit all the data into memory at once (to compute the PCA) so we
% subsample the training data.
fileList = FindPitchFiles(databaseToUse);
filesToUse = 1:10:length(fileList);         % Big training exercise
filesToUse = 1:100:length(fileList);        % Small training example
tic;
[pitchModel, fileList] = ComputePitchModel('graz', pcaThresh, [], filesToUse);
save newPitchModel pitchModel
toc;

FindFiles: Checking ../PitchTrackingDatabase/Graz for .f0
FindFiles: Checking ../PitchTrackingDatabase/Graz/FEMALE for .f0
FindFiles: Checking ../PitchTrackingDatabase/Graz/FEMALE/LAR for .f0
FindFiles: Checking ../PitchTrackingDatabase/Graz/FEMALE/LAR/F01 for .f0
FindFiles: Checking ../PitchTrackingDatabase/Graz/FEMALE/LAR/F02 for .f0
FindFiles: Checking ../PitchTrackingDatabase/Graz/FEMALE/LAR/F03 for .f0
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FindFiles: Checking ../PitchTrackingDatabase/Graz/FEMALE/MIC for .f0
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FindFiles: Checking ../PitchTrackingDatabase/Graz for .f0
FindFiles: Checking ../PitchTrackingDatabase/Graz/FEMALE for .f0
FindFiles: Checking ../PitchTrackingDatabase/Graz/FEMALE/LAR for .f0
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FindFiles: Checking ../PitchTrackingDatabase/Graz/MALE/REF for .f0
FindFiles: Checking ../PitchTrackingDatabase/Graz/MALE/REF/M01 for .f0
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Finding pitch transition probabilities for 48 files.
Computing correlogram features for 48 sounds.....
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F01/mic_F01_sa1.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F01/mic_F01_si551.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F01/mic_F01_sx19.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F02/mic_F02_si704.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F02/mic_F02_si804.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F03/mic_F03_si837.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F03/mic_F03_si937.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F03/mic_F03_sx137.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F04/mic_F04_si1110.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F04/mic_F04_sx139.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F05/mic_F05_si1263.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F05/mic_F05_si1363.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F06/mic_F06_si1416.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F06/mic_F06_si1516.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F06/mic_F06_sx257.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F07/mic_F07_si1669.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F07/mic_F07_si1769.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F08/mic_F08_si1822.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F08/mic_F08_si1924.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F09/mic_F09_si1977.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F09/mic_F09_si2077.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F09/mic_F09_sx386.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F10/mic_F10_si2230.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F10/mic_F10_si2330.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M01/mic_M01_si493.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M01/mic_M01_si593.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M02/mic_M02_si646.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M02/mic_M02_si746.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M02/mic_M02_sx63.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M03/mic_M03_si879.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M03/mic_M03_si979.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M04/mic_M04_si1052.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M04/mic_M04_si1152.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M04/mic_M04_sx181.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M05/mic_M05_si1305.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M05/mic_M05_sx190.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M06/mic_M06_si1458.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M06/mic_M06_si1558.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M07/mic_M07_si1611.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M07/mic_M07_si1711.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M07/mic_M07_sx308.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M08/mic_M08_si1864.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M08/mic_M08_si1964.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M09/mic_M09_si2017.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M09/mic_M09_si2117.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M10/mic_M10_si2170.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M10/mic_M10_si2270.mat...
 Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M10/mic_M10_sx435.mat...
Finding PCA model with a energy threshold of 5 x Mean Energy
Channel 1: Computing the principal components of a 512 x 10257 matrix
Channel 2: Computing the principal components of a 512 x 10257 matrix
Channel 3: Computing the principal components of a 512 x 10257 matrix
Channel 4: Computing the principal components of a 512 x 10257 matrix
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Channel 48: Computing the principal components of a 512 x 10257 matrix
Finding the MLP model to transform correlogram into pitch probs.
Cycle    1  Error 384017.579882  Scale 1.000000e+00
Cycle    2  Error 247910.885624  Scale 5.000000e-01
Cycle    3  Error 84778.699611  Scale 2.500000e-01
Cycle    4  Error 68258.050248  Scale 1.250000e-01
Cycle    5  Error 51694.628885  Scale 6.250000e-02
Cycle    6  Error 49270.981352  Scale 3.125000e-02
Cycle    7  Error 45128.698215  Scale 1.562500e-02
Cycle    8  Error 43585.996495  Scale 7.812500e-03
Cycle    9  Error 41059.158424  Scale 3.906250e-03
Cycle   10  Error 40370.004812  Scale 1.953125e-03
Cycle   11  Error 39716.878648  Scale 9.765625e-04
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Cycle   14  Error 36982.788449  Scale 1.220703e-04
Cycle   15  Error 36422.579200  Scale 6.103516e-05
Cycle   16  Error 35689.625875  Scale 3.051758e-05
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Cycle   21  Error 34131.079231  Scale 9.536743e-07
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Cycle   92  Error 20696.736840  Scale 2.842171e-14
Cycle   93  Error 20539.600097  Scale 1.421085e-14
Cycle   94  Error 20425.506331  Scale 7.105427e-15
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Cycle   99  Error 19804.063562  Scale 1.000000e-15
Cycle  100  Error 19684.189898  Scale 1.000000e-15
Maximum number of iterations has been exceeded
Good responses average 0.801333, bad response average 0.00292607, ratio is 273.86.
Elapsed time is 1410.707479 seconds.

Measure new pitch model's performance

Measure performance of this model using a set of test data. Most importantly, this function returns a graph that shows two things: 1) the effect of changing the importance of the transition probability matrix (larger, to the right, means prefer normal transitions), and 2)the importance of the prior probabilities (different lines).

global gPlayPitchSounds
gPlayPitchSounds = 0;
tic;
filesToTest = 2:40:length(fileList);    % Select a different subset for testing.
filesToTest = 2:100:length(fileList);   % Select a small subset for testing.
[pitchTrackingError, voicedError, unvoicedError] = ...
    MeasurePitchPerformance(fileList(filesToTest), pitchModel);
toc;

Precomputing features for file 1/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F01/mic_F01_sa2.mat...
Precomputing features for file 2/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F01/mic_F01_si552.mat...
Precomputing features for file 3/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F01/mic_F01_sx20.mat...
Precomputing features for file 4/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F02/mic_F02_si705.mat...
Precomputing features for file 5/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F02/mic_F02_si805.mat...
Precomputing features for file 6/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F03/mic_F03_si838.mat...
Precomputing features for file 7/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F03/mic_F03_si938.mat...
Precomputing features for file 8/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F03/mic_F03_sx93.mat...
Precomputing features for file 9/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F04/mic_F04_si1111.mat...
Precomputing features for file 10/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F04/mic_F04_sx140.mat...
Precomputing features for file 11/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F05/mic_F05_si1264.mat...
Precomputing features for file 12/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F05/mic_F05_si1364.mat...
Precomputing features for file 13/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F06/mic_F06_si1417.mat...
Precomputing features for file 14/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F06/mic_F06_si1517.mat...
Precomputing features for file 15/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F06/mic_F06_sx258.mat...
Precomputing features for file 16/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F07/mic_F07_si1670.mat...
Precomputing features for file 17/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F07/mic_F07_si1770.mat...
Precomputing features for file 18/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F08/mic_F08_si1823.mat...
Precomputing features for file 19/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F08/mic_F08_si1925.mat...
Precomputing features for file 20/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F09/mic_F09_si1978.mat...
Precomputing features for file 21/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F09/mic_F09_si2078.mat...
Precomputing features for file 22/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F09/mic_F09_sx387.mat...
Precomputing features for file 23/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F10/mic_F10_si2231.mat...
Precomputing features for file 24/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/FEMALE/MIC/F10/mic_F10_si2331.mat...
Precomputing features for file 25/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M01/mic_M01_si494.mat...
Precomputing features for file 26/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M01/mic_M01_si594.mat...
Precomputing features for file 27/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M02/mic_M02_si647.mat...
Precomputing features for file 28/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M02/mic_M02_si747.mat...
Precomputing features for file 29/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M02/mic_M02_sx64.mat...
Precomputing features for file 30/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M03/mic_M03_si880.mat...
Precomputing features for file 31/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M03/mic_M03_si980.mat...
Precomputing features for file 32/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M04/mic_M04_si1053.mat...
Precomputing features for file 33/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M04/mic_M04_si1153.mat...
Precomputing features for file 34/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M04/mic_M04_sx182.mat...
Precomputing features for file 35/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M05/mic_M05_si1306.mat...
Precomputing features for file 36/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M05/mic_M05_sx191.mat...
Precomputing features for file 37/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M06/mic_M06_si1459.mat...
Precomputing features for file 38/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M06/mic_M06_si1559.mat...
Precomputing features for file 39/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M07/mic_M07_si1612.mat...
Precomputing features for file 40/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M07/mic_M07_si1712.mat...
Precomputing features for file 41/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M07/mic_M07_sx309.mat...
Precomputing features for file 42/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M08/mic_M08_si1865.mat...
Precomputing features for file 43/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M08/mic_M08_sx318.mat...
Precomputing features for file 44/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M09/mic_M09_si2018.mat...
Precomputing features for file 45/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M09/mic_M09_si2118.mat...
Precomputing features for file 46/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M10/mic_M10_si2171.mat...
Precomputing features for file 47/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M10/mic_M10_si2271.mat...
Precomputing features for file 48/48.
Reading correlogram data from ../PitchTrackingDatabase/Graz/MALE/MIC/M10/mic_M10_sx436.mat...
All done doing the ComputeAllFeatures.
Elapsed time is 2450.879444 seconds.