Question

Deep leraning/LSTM/Matlab

There is a Matlab code that is doing the following steps for deep learning and applying LSTM, I need to change first three steps to use our dataset to train this model and you don't need to change other.

I need to apply that for .ogg audio files so Create and Use some audio files with .ogg format as sample data and give me the code.

The following steps is for your information:

1. Three classes of audio signals are generated and labeled as 'white', 'brown', and 'pink'. Each class has 1000 samples.
2. 800 samples from each class are used as the training samples to train the deep neural network, so total 800*3=2400 samples in the training dataset. Their labels are their class names 'white', 'brown', and 'pink'. (Lines 29 and 30)
3. 200 samples from each class are used as the validation samples to test the performance of deep neural network, so total 600 samples in the validation dataset. Their labels are their class names 'white', 'brown', and 'pink' (Lines 32 and 33)
4. Extract features from the training dataset and validation dataset.
5. define the structure of the neural network model (LSTM)
6. set training options
7. train the model iteratively using the training dataset and test the model using the validation dataset every iteration.
8. finish training and get the trained model.
9. generate test dataset and use the trained model to classify the test dataset into three classes, 'white', 'brown', and 'pink'.

Code:

fs = 44.1e3;

duration = 0.5;

N = duration*fs;

wNoise = 2*rand([N,1000]) - 1;

wLabels = repelem(categorical("white"),1000,1);

bNoise = filter(1,[1,-0.999],wNoise);

bNoise = bNoise./max(abs(bNoise),[],'all');

bLabels = repelem(categorical("brown"),1000,1);

pNoise = pinknoise([N,1000]);

pLabels = repelem(categorical("pink"),1000,1)

sound(wNoise(:,1),fs)

melSpectrogram(wNoise(:,1),fs)

title('White Noise')

sound(bNoise(:,1),fs)

melSpectrogram(bNoise(:,1),fs)

title('Brown Noise')

sound(pNoise(:,1),fs)

melSpectrogram(pNoise(:,1),fs)

title('Pink Noise')

featuresTrain = extract(aFE,audioTrain);

[numHopsPerSequence,numFeatures,numSignals] = size(featuresTrain)

audioTrain = [wNoise(:,1:800),bNoise(:,1:800),pNoise(:,1:800)];

labelsTrain = [wLabels(1:800);bLabels(1:800);pLabels(1:800)];

audioValidation = [wNoise(:,801:end),bNoise(:,801:end),pNoise(:,801:end)];

labelsValidation = [wLabels(801:end);bLabels(801:end);pLabels(801:end)];

aFE = audioFeatureExtractor("SampleRate",fs, ...

"SpectralDescriptorInput","melSpectrum", ...

"spectralCentroid",true, ...

"spectralSlope",true);

featuresTrain = permute(featuresTrain,[2,1,3]);

featuresTrain = squeeze(num2cell(featuresTrain,[1,2]));

numSignals = numel(featuresTrain)

[numFeatures,numHopsPerSequence] = size(featuresTrain{1})

featuresValidation = extract(aFE,audioValidation);

featuresValidation = permute(featuresValidation,[2,1,3]);

featuresValidation = squeeze(num2cell(featuresValidation,[1,2]));

layers = [ ...

sequenceInputLayer(numFeatures)

lstmLayer(50,"OutputMode","last")

fullyConnectedLayer(numel(unique(labelsTrain)))

softmaxLayer

classificationLayer];

options = trainingOptions("adam", ...

"Shuffle","every-epoch", ...

"ValidationData",{featuresValidation,labelsValidation}, ...

"Plots","training-progress", ...

"Verbose",false);

net = trainNetwork(featuresTrain,labelsTrain,layers,options);

wNoiseTest = 2*rand([N,1]) - 1;

classify(net,extract(aFE,wNoiseTest)')

bNoiseTest = filter(1,[1,-0.999],wNoiseTest);

bNoiseTest= bNoiseTest./max(abs(bNoiseTest),[],'all');

classify(net,extract(aFE,bNoiseTest)')

pNoiseTest = pinknoise(N);

classify(net,extract(aFE,pNoiseTest)')

Answer 1

sound(wNoise(:,1),fs)
title('White Noise')

melSpectrogram(wNoise(:,1),fs)

sound(bNoise(:,1),fs) title('Brown Noise')

melSpectrogram(bNoise(:,1),fs)

sound(pNoise(:,1),fs) title('Pink Noise')

melSpectrogram(pNoise(:,1),fa)

audioTrain = [wNoise(:,1:800),bNoise(:,1:800),pNoise(:,1:800)];
labelsTrain = [wLabels(1:800);bLabels(1:800);pLabels(1:800)];

audioValidation = [wNoise(:,801:end),bNoise(:,801:end),pNoise(:,801:end)];
labelsValidation = [wLabels(801:end);bLabels(801:end);pLabels(801:end)];

aFE = audioFeatureExtractor("SampleRate",fs, ...
    "SpectralDescriptorInput","melSpectrum", ...
    "spectralCentroid",true, ...
    "spectralSlope",true);

featuresTrain = extract(aFE,audioTrain);
[numHopsPerSequence,numFeatures,numSignals] = size(featuresTrain)

featuresTrain = permute(featuresTrain,[2,1,3]);
featuresTrain = squeeze(num2cell(featuresTrain,[1,2]));

numSignals = numel(featuresTrain)

numFeatures,numHopsPerSequence] = size(featuresTrain{1})

featuresValidation = extract(aFE,audioValidation);
featuresValidation = permute(featuresValidation,[2,1,3]);
featuresValidation = squeeze(num2cell(featuresValidation,[1,2]));

layers = [ ...

    sequenceInputLayer(numFeatures)
    lstmLayer(50,"OutputMode","last")
    fullyConnectedLayer(numel(unique(labelsTrain)))
    softmaxLayer
    classificationLayer];

options = trainingOptions("adam", ...
    "Shuffle","every-epoch", ...
    "ValidationData",{featuresValidation,labelsValidation}, ...
    "Plots","training-progress", ...
    "Verbose",false);

net = trainNetwork(featuresTrain,labelsTrain,layers,options);

For testing the network:

wNoiseTest = 2*rand([N,1]) - 1;
classify(net,extract(aFE,wNoiseTest)')

bNoiseTest = filter(1,[1,-0.999],wNoiseTest);
bNoiseTest= bNoiseTest./max(abs(bNoiseTest),[],'all');
classify(net,extract(aFE,bNoiseTest)')

pNoiseTest = pinknoise(N);
classify(net,extract(aFE,pNoiseTest)')

In first go 

Your answer will be:

ans​= categorial white 

In second go

Your answer will be:

ans= categorial brown

In third go

Your answer will be:

ans= categorial pink