Can I call my .m file code which is in regard with Deep Learning and applying LSTM?

I want to use my code for LSTM and audio classification in Python. Can I use that directly without the need to write the code from scrarch in Python?

If there is anyway to call my .m file in Pyhton please let me know step by step.

Thanks

Answer 1

[Swetha Polemoni]

Hi

You may find this answer helpful.

Answer 2

[Pooyan Mobtahej]

Hi

Here is the code that I need to imoplement in Python ?

How can I convert it Do you have any suggestion for data importing part as well as applying LSTM?

clear all
close all

TrainRatio=0.1;
ValidationRatio=0.1;

folder='/Users/pooyan/Desktop/cmms/class/normal/';  % change this path to your normal data folder
audio_files=dir(fullfile(folder,'*.ogg'));
nfileNum=length(audio_files);
nfileNum=1000
normal=[];
for i = 3600:4599
    normal_name = [folder audio_files(i).name]; 
    normal(i-3599,:) = audioread(normal_name);   
end 
normal=normal';
nLabels = repelem(categorical("normal"),nfileNum,1);

folder='/Users/pooyan/Desktop/cmms/class/anomaly/'; % change this path to your anomaly data folder
audio_files=dir(fullfile(folder,'*.ogg'));
afileNum=length(audio_files);
afileNum=100

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)
predict(i) = classify(net,TestFeatureIn);

end

plotconfusion(labelsTest,predict')