Adding confusion matrix code for LSTM classification on audio files in Matlab

In the code i apply LSTM and get results for large datasets of .ogg audio files (datasets) in Matlab, Data can be separated into three parts. For example, 80% of all normal and anomaly signals for training (2 classes), 10% for validation, and 10% for testing. Now I want to add confusion matrix for the results. Please advise me.

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