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Add confusion matrix to my cross validated code for LSTM classification

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Pooyan Mobtahej
Pooyan Mobtahej on 23 Nov 2020
Answered: Divya Gaddipati on 31 Dec 2020
As the code below I have used LSTM for classifiction of audio data and added cross validation now I would like to show all the results from cross validation in one confusion matrix, how can I do that?
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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=10
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));
AllData = [normal anomaly];
Labels=[nLabels; aLabels];
% K indicates K-fold cross validation
K=2;
cv = cvpartition(Labels,'KFold',K);
% nTrainNum = round(nfileNum*TrainRatio*0.1);
% aTrainNum = round(afileNum*TrainRatio*0.1);
% nValidationNum = round(nfileNum*ValidationRatio*0.1);
% aValidationNum = round(afileNum*ValidationRatio*0.1);
for i=1:K
audioTest = AllData(:, cv.test(i));
labelsTest = Labels(cv.test(i));
audioTrainValidation = AllData(:, ~cv.test(i));
labelsTrainValidation = Labels(~cv.test(i));
% Vp: 10% from training dataset used for validation;
Vp=0.1;
TVL=length(labelsTrainValidation);
ValidationIndex = randperm(TVL, floor(TVL*Vp));
TrainIndex=1:TVL;
TrainIndex(ValidationIndex)=[];
audioTrain = audioTrainValidation(:, TrainIndex);
labelsTrain = labelsTrainValidation(TrainIndex);
audioValidation = audioTrainValidation(:, ValidationIndex);
labelsValidation = labelsTrainValidation(ValidationIndex);
% audioTrain = [normal(:,((i-1)*nTrainNum)+1:i*nTrainNum),anomaly(:,((i-1)*aTrainNum)+1:i*aTrainNum)];
% labelsTrain = [nLabels(((i-1)*nTrainNum)+1:i*nTrainNum);aLabels(((i-1)*aTrainNum)+1:i*aTrainNum)];
%
% audioValidation = [normal(:,i*(nTrainNum+1:nTrainNum+nValidationNum)),anomaly(:,i*(aTrainNum+1:aTrainNum+aValidationNum))];
% labelsValidation = [nLabels(i*(nTrainNum+1):i*(nTrainNum+nValidationNum));aLabels(i*(aTrainNum+1:aTrainNum+aValidationNum))];
%
% audioTest = [normal(:,i*(nTrainNum+nValidationNum+1):end),anomaly(:,i*(aTrainNum+aValidationNum+1):end)];
% labelsTest = [nLabels(i*(nTrainNum+nValidationNum+1):end); aLabels(i*(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)
predict(i) = classify(net,TestFeatureIn)
%labelsPred = categorical(classify(net,TestFeatureIn))
end
%Confusion Matrix Chart
%plotconfusion(labelsTest,predict')
C = confusionmat(labelsTest,predict')
confusionchart(labelsTest,predict')
end

Answers (1)

Divya Gaddipati
Divya Gaddipati on 31 Dec 2020
Hi,
You can accumulate results at the end of loop.
catLabels = [catLabels; labelsTest];
catPredictions = [catPredictions; predict'];
Then, outside the loop, you can calculate the confusion matrix
C = confusionmat(catLabels,catPredictions);
confusionchart(catLabels,catPredictions);

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