Adding confusion code for LSTM classification on audio files in Matlab

1 view (last 30 days)
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

Sign in to comment.

More Answers (0)

Categories

Find more on Sequence and Numeric Feature Data Workflows in Help Center and File Exchange

Tags

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!