[人工智能]matlab 深度学习做声音分类

1、内容简介

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深度学习做声音分类

2、内容说明

提取声音特征，然后通过CNN进行分类

3、仿真分析

[normal_01_L,~] = audioread('正常样本2s_01_L.wav');

[normal_02_L,~] = audioread('正常样本2s_02_L.wav');
[qikong_01_L,fs] = audioread('气孔样本2s_01_L.wav');

len = 8e4;

numTrain = 800;
audioTrain = [data_1(:,1:numTrain),data_2(:,1:numTrain),data_3(:,1:numTrain)];
labelsTrain = [data_1_Label(1:numTrain);data_2_Label(1:numTrain);data_3_Label(1:numTrain)];

audioValidation = [data_1(:,numTrain+1:end),data_2(:,numTrain+1:end),data_3(:,numTrain+1:end)];
labelsValidation = [data_1_Label(numTrain+1:end);data_2_Label(numTrain+1:end);data_3_Label(numTrain+1:end)];

aFE = audioFeatureExtractor("SampleRate",fs, ...
? ? "SpectralDescriptorInput","melSpectrum", ...
? ? "spectralCentroid",true, ...
? ? "spectralSlope",true);

featuresTrain = extract(aFE,audioTrain);
[numHopsPerSequence,numFeatures,numSignals] = size(featuresTrain)

featuresTrain = permute(featuresTrain,[2,1,3]);
featuresTrain = squeeze(num2cell(featuresTrain,[1,2]));

numSignals = numel(featuresTrain)

[numFeatures,numHopsPerSequence] = size(featuresTrain{1})

featuresValidation = extract(aFE,audioValidation);
featuresValidation = permute(featuresValidation,[2,1,3]);
featuresValidation = squeeze(num2cell(featuresValidation,[1,2]));

layers = [ ...
? ? sequenceInputLayer(numFeatures)
? ? lstmLayer(50,"OutputMode","last")
? ? fullyConnectedLayer(numel(unique(labelsTrain)))
? ? softmaxLayer
? ? classificationLayer];

options = trainingOptions("adam", ...
? ? "Shuffle","every-epoch", ...
? ? "ValidationData",{featuresValidation,labelsValidation}, ...
? ? "Plots","training-progress", ...
? ? "MaxEpochs", 100,...
? ? "Verbose",false);

net = trainNetwork(featuresTrain,labelsTrain,layers,options);
?

4、参考论文

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