Adding confusion code for LSTM classification on audio files in Matlab

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in the following code I have applied LSTM on audio files. Now I want to add confusion matrix for the results. Please advise me.
clear all
close all
TrainRatio=0.8;
ValidationRatio=0.1;
folder='/Users/pooyan/Documents/normal/'; % change this path to your normal data folder
audio_files=dir(fullfile(folder,'*.ogg'));
nfileNum=length(audio_files);
%nfileNum=200
normal=[];
for i = 1:nfileNum
normal_name = [folder audio_files(i).name];
normal(i,:) = audioread(normal_name);
end
normal=normal';
nLabels = repelem(categorical("normal"),nfileNum,1);
folder='/Users/pooyan/Documents/anomaly/'; % change this path to your anomaly data folder
audio_files=dir(fullfile(folder,'*.ogg'));
afileNum=length(audio_files);
anomaly=[];
for i = 1:afileNum
anomaly_name = [folder audio_files(i).name];
anomaly(i,:) = audioread(anomaly_name);
end
anomaly=anomaly';
aLabels = repelem(categorical("anomaly"),afileNum,1);
% randomize the inputs if necessary
% normal=normal(:,randperm(nfileNum, nfileNum));
% anomaly=anomaly(:,randperm(afileNum, afileNum));
nTrainNum = round(nfileNum*TrainRatio);
aTrainNum = round(afileNum*TrainRatio);
nValidationNum = round(nfileNum*ValidationRatio);
aValidationNum = round(afileNum*ValidationRatio);
audioTrain = [normal(:,1:nTrainNum),anomaly(:,1:aTrainNum)];
labelsTrain = [nLabels(1:nTrainNum);aLabels(1:aTrainNum)];
audioValidation = [normal(:,nTrainNum+1:nTrainNum+nValidationNum),anomaly(:,aTrainNum+1:aTrainNum+aValidationNum)];
labelsValidation = [nLabels(nTrainNum+1:nTrainNum+nValidationNum);aLabels(aTrainNum+1:aTrainNum+aValidationNum)];
audioTest = [normal(:,nTrainNum+nValidationNum+1:end),anomaly(:,aTrainNum+aValidationNum+1:end)];
labelsTest = [nLabels(nTrainNum+nValidationNum+1:end); aLabels(aTrainNum+aValidationNum+1:end)];
fs=44100;
% Create an audioFeatureExtractor object
%to extract the centroid and slope of the mel spectrum over time.
aFE = audioFeatureExtractor("SampleRate",fs, ... %Fs
"SpectralDescriptorInput","melSpectrum", ...
"spectralCentroid",true, ...
"spectralSlope",true);
featuresTrain = extract(aFE,audioTrain);
[numHopsPerSequence,numFeatures,numSignals] = size(featuresTrain);
numHopsPerSequence;
numFeatures;
numSignals;
%treat the extracted features as sequences and use a
%sequenceInputLayer as the first layer of your deep learning model.
featuresTrain = permute(featuresTrain,[2,1,3]); %permute switching dimensions in array
featuresTrain = squeeze(num2cell(featuresTrain,[1,2]));%remove dimensions
numSignals = numel(featuresTrain); %number of signals of normal and anomalies
[numFeatures,numHopsPerSequence] = size(featuresTrain{1});
%Extract the validation features.
featuresValidation = extract(aFE,audioValidation);
featuresValidation = permute(featuresValidation,[2,1,3]);
featuresValidation = squeeze(num2cell(featuresValidation,[1,2]));
%Define the network architecture.
layers = [ ...
sequenceInputLayer(numFeatures)
lstmLayer(50,"OutputMode","last")
fullyConnectedLayer(numel(unique(labelsTrain))) %%labelTrain=audio
softmaxLayer
classificationLayer];
%To define the training options
options = trainingOptions("adam", ...
"Shuffle","every-epoch", ...
"ValidationData",{featuresValidation,labelsValidation}, ... %%labelValidatin=audioValidation
"Plots","training-progress", ...
"Verbose",false);
%To train the network
net = trainNetwork(featuresTrain,labelsTrain,layers,options);
%Test the network %10 preccent
%classify(net,permute(extract(aFE,audioTest),[2 257 35]))
TestFeature=extract(aFE, audioTest);
for i=1:size(TestFeature, 3)
TestFeatureIn = TestFeature(:,:,i)';
classify(net,TestFeatureIn)
end

Accepted Answer

Brian Hemmat
Brian Hemmat on 9 Nov 2020
Your output from classify will be your predicted labels, your the targets will be the known labels (it looks like 'labelsTest' in your code.
  7 Comments
Pooyan Mobtahej
Pooyan Mobtahej on 10 Nov 2020
Should I make an array for put the classification data in it for prediction ? if you let me know the function that would be great

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