基于小波散射和深度学习的口述数字识别

downloadFolder = matlab.internal.examples.downloadSupportFile("audio","FSDD.zip");
dataFolder = tempdir;
unzip(downloadFolder,dataFolder)
dataset = fullfile(dataFolder,"FSDD");

ads = audioDatastore(dataset,IncludeSubfolders=true);

ads.Labels = helpergenLabels(ads);
summary(ads.Labels)

LenSig = zeros(numel(ads.Files),1);
nr = 1;
while hasdata(ads)
    digit = read(ads);
    LenSig(nr) = numel(digit);
    nr = nr+1;
end
reset(ads)
histogram(LenSig)
grid on
xlabel("Signal Length (Samples)")
ylabel("Frequency")

sf = waveletScattering(SignalLength=8192,InvarianceScale=0.22, ...
    SamplingFrequency=8000,OversamplingFactor=2);

rng("default")
ads = shuffle(ads);
[adsTrain,adsTest] = splitEachLabel(ads,0.8);
countEachLabel(adsTrain)

countEachLabel(adsTest)

Xtrain = [];
scatds_Train = transform(adsTrain,@(x)helperReadSPData(x));
while hasdata(scatds_Train)
    smat = read(scatds_Train);
    Xtrain = cat(2,Xtrain,smat);
end

Xtest = [];
scatds_Test = transform(adsTest,@(x)helperReadSPData(x));
while hasdata(scatds_Test)
    smat = read(scatds_Test);
    Xtest = cat(2,Xtest,smat);   
end

Strain = sf.featureMatrix(Xtrain);
Stest = sf.featureMatrix(Xtest);

TrainFeatures = Strain(2:end,:,:);
TrainFeatures = squeeze(mean(TrainFeatures,2))';
TestFeatures = Stest(2:end,:,:);
TestFeatures = squeeze(mean(TestFeatures,2))';

template = templateSVM(...
    KernelFunction="polynomial", ...
    PolynomialOrder=2, ...
    KernelScale="auto", ...
    BoxConstraint=1, ...
    Standardize=true);
classificationSVM = fitcecoc( ...
    TrainFeatures, ...
    adsTrain.Labels, ...
    Learners=template, ...
    Coding="onevsone", ...
    ClassNames=categorical({'0'; '1'; '2'; '3'; '4'; '5'; '6'; '7'; '8'; '9'}));

partitionedModel = crossval(classificationSVM,KFold=5);
[validationPredictions, validationScores] = kfoldPredict(partitionedModel);
validationAccuracy = (1 - kfoldLoss(partitionedModel,LossFun="ClassifError"))*100

predLabels = predict(classificationSVM,TestFeatures);
testAccuracy = sum(predLabels==adsTest.Labels)/numel(predLabels)*100 %#ok<NASGU>

figure(Units="normalized",Position=[0.2 0.2 0.5 0.5])
ccscat = confusionchart(adsTest.Labels,predLabels);
ccscat.Title = "Wavelet Scattering Classification";
ccscat.ColumnSummary = "column-normalized";
ccscat.RowSummary = "row-normalized";

TrainFeatures = Strain(2:end,:,:);
TrainFeatures = squeeze(num2cell(TrainFeatures,[1 2]));
TestFeatures = Stest(2:end,:,:);
TestFeatures = squeeze(num2cell(TestFeatures, [1 2]));

[inputSize, ~] = size(TrainFeatures{1});
YTrain = adsTrain.Labels;

numHiddenUnits = 512;
numClasses = numel(unique(YTrain));

layers = [ ...
    sequenceInputLayer(inputSize)
    lstmLayer(numHiddenUnits,OutputMode="last")
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];

maxEpochs = 300;
miniBatchSize = 50;

options = trainingOptions("adam", ...
    InitialLearnRate=0.0001,...
    MaxEpochs=maxEpochs, ...
    MiniBatchSize=miniBatchSize, ...
    SequenceLength="shortest", ...
    Shuffle="every-epoch",...
    Verbose=false, ...
    Plots="training-progress");

net = trainNetwork(TrainFeatures,YTrain,layers,options);

predLabels = classify(net,TestFeatures);
testAccuracy = sum(predLabels==adsTest.Labels)/numel(predLabels)*100 %#ok<NASGU>

YTrain = adsTrain.Labels;
YTest = adsTest.Labels;

if ~exist("results/",'dir')
    mkdir results
end

optVars = [
    optimizableVariable(InitialLearnRate=[1e-5, 1e-1],Transform="log")
    optimizableVariable(NumHiddenUnits=[10, 1000],Type="integer")
    ];

ObjFcn = helperBayesOptLSTM(TrainFeatures,YTrain,TestFeatures,YTest);

optimizeCondition = false;
if optimizeCondition
    BayesObject = bayesopt(ObjFcn,optVars, ...
            MaxObjectiveEvaluations=15, ...
            IsObjectiveDeterministic=false, ...
            UseParallel=true); %#ok<UNRCH>
else
    url = "http://ssd.mathworks.com/supportfiles/audio/SpokenDigitRecognition.zip";
    downloadNetFolder = tempdir;
    netFolder = fullfile(downloadNetFolder,"SpokenDigitRecognition");
    if ~exist(netFolder,"dir")
        disp("Downloading pretrained network (1 file - 12 MB) ...")
        unzip(url,downloadNetFolder)
    end
    load(fullfile(netFolder,"0.02.mat"))
end

numHiddenUnits = 768;
numClasses = numel(unique(YTrain));

layers = [ ...
    sequenceInputLayer(inputSize)
    lstmLayer(numHiddenUnits,OutputMode="last")
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];

maxEpochs = 300;
miniBatchSize = 50;

options = trainingOptions("adam", ...
    InitialLearnRate=2.198827960269379e-04, ...
    MaxEpochs=maxEpochs, ...
    MiniBatchSize=miniBatchSize, ...
    SequenceLength="shortest", ...
    Shuffle="every-epoch", ...
    Verbose=false, ...
    Plots="training-progress");

net = trainNetwork(TrainFeatures,YTrain,layers,options);

predLabels = classify(net,TestFeatures);
testAccuracy = sum(predLabels==adsTest.Labels)/numel(predLabels)*100

segmentDuration = 8192*(1/8000);
frameDuration = 0.22;
hopDuration = 0.01;
numBands = 40;

reset(adsTrain)
reset(adsTest)

epsil = 1e-6;
XTrain = helperspeechSpectrograms(adsTrain,segmentDuration,frameDuration,hopDuration,numBands);

XTrain = log10(XTrain + epsil);

XTest = helperspeechSpectrograms(adsTest,segmentDuration,frameDuration,hopDuration,numBands);

XTest = log10(XTest + epsil);

YTrain = adsTrain.Labels;
YTest = adsTest.Labels;

sz = size(XTrain);
specSize = sz(1:2);
imageSize = [specSize 1];

numClasses = numel(categories(YTrain));

dropoutProb = 0.2;
numF = 12;
layers = [
    imageInputLayer(imageSize)

    convolution2dLayer(5,numF,Padding="same")
    batchNormalizationLayer
    reluLayer

    maxPooling2dLayer(3,Stride=2,Padding="same")

    convolution2dLayer(3,2*numF,Padding="same")
    batchNormalizationLayer
    reluLayer

    maxPooling2dLayer(3,Stride=2,Padding="same")

    convolution2dLayer(3,4*numF,Padding="same")
    batchNormalizationLayer
    reluLayer

    maxPooling2dLayer(3,Stride=2,Padding="same")

    convolution2dLayer(3,4*numF,Padding="same")
    batchNormalizationLayer
    reluLayer
    convolution2dLayer(3,4*numF,Padding="same")
    batchNormalizationLayer
    reluLayer

    maxPooling2dLayer(2)

    dropoutLayer(dropoutProb)
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer(Classes=categories(YTrain));
    ];

miniBatchSize = 50;
options = trainingOptions("adam", ...
    InitialLearnRate=1e-4, ...
    MaxEpochs=30, ...
    MiniBatchSize=miniBatchSize, ...
    Shuffle="every-epoch", ...
    Plots="training-progress", ...
    Verbose=false, ...
    ExecutionEnvironment="cpu");

trainedNet = trainNetwork(XTrain,YTrain,layers,options);

[Ypredicted,probs] = classify(trainedNet,XTest,ExecutionEnvironment="CPU");
cnnAccuracy = sum(Ypredicted==YTest)/numel(YTest)*100

figure(Units="normalized",Position=[0.2 0.2 0.5 0.5]);
ccDCNN = confusionchart(YTest,Ypredicted);
ccDCNN.Title = "Confusion Chart for DCNN";
ccDCNN.ColumnSummary = "column-normalized";
ccDCNN.RowSummary = "row-normalized";

function Labels = helpergenLabels(ads)
% This function is only for use in Wavelet Toolbox examples. It may be
% changed or removed in a future release.
tmp = cell(numel(ads.Files),1);
expression = "[0-9]+_";
for nf = 1:numel(ads.Files)
    idx = regexp(ads.Files{nf},expression);
    tmp{nf} = ads.Files{nf}(idx);
end
Labels = categorical(tmp);
end

function x = helperReadSPData(x)
% This function is only for use Wavelet Toolbox examples. It may change or
% be removed in a future release.

N = numel(x);
if N > 8192
    x = x(1:8192);
elseif N < 8192
    pad = 8192-N;
    prepad = floor(pad/2);
    postpad = ceil(pad/2);
    x = [zeros(prepad,1) ; x ; zeros(postpad,1)];
end
x = x./max(abs(x));

end

function x = helperBayesOptLSTM(X_train, Y_train, X_val, Y_val)
% This function is only for use in the
% "Spoken Digit Recognition with Wavelet Scattering and Deep Learning"
% example. It may change or be removed in a future release.
x = @valErrorFun;

    function [valError,cons, fileName] = valErrorFun(optVars)
        %% LSTM Architecture
        [inputSize,~] = size(X_train{1});
        numClasses = numel(unique(Y_train));

        layers = [ ...
            sequenceInputLayer(inputSize)
            bilstmLayer(optVars.NumHiddenUnits,OutputMode="last") % Using number of hidden layers value from optimizing variable
            fullyConnectedLayer(numClasses)
            softmaxLayer
            classificationLayer];
        
        % Plots not displayed during training
        options = trainingOptions("adam", ...
            InitialLearnRate=optVars.InitialLearnRate, ... % Using initial learning rate value from optimizing variable
            MaxEpochs=300, ...
            MiniBatchSize=30, ...
            SequenceLength="shortest", ...
            Shuffle="never", ...
            Verbose=false);
        
        %% Train the network
        net = trainNetwork(X_train, Y_train, layers, options);
        %% Training accuracy
        X_val_P = net.classify(X_val);
        accuracy_training  = sum(X_val_P == Y_val)./numel(Y_val);
        valError = 1 - accuracy_training;
        %% save results of network and options in a MAT file in the results folder along with the error value
        fileName = fullfile("results", num2str(valError) + ".mat");
        save(fileName,"net","valError","options")     
        cons = [];
    end % end for inner function
end % end for outer function

function X = helperspeechSpectrograms(ads,segmentDuration,frameDuration,hopDuration,numBands)
% This function is only for use in the 
% "Spoken Digit Recognition with Wavelet Scattering and Deep Learning"
% example. It may change or be removed in a future release.
%
% helperspeechSpectrograms(ads,segmentDuration,frameDuration,hopDuration,numBands)
% computes speech spectrograms for the files in the datastore ads.
% segmentDuration is the total duration of the speech clips (in seconds),
% frameDuration the duration of each spectrogram frame, hopDuration the
% time shift between each spectrogram frame, and numBands the number of
% frequency bands.
disp("Computing speech spectrograms...");

numHops = ceil((segmentDuration - frameDuration)/hopDuration);
numFiles = length(ads.Files);
X = zeros([numBands,numHops,1,numFiles],"single");

for i = 1:numFiles
    
    [x,info] = read(ads);
    x = normalizeAndResize(x);
    fs = info.SampleRate;
    frameLength = round(frameDuration*fs);
    hopLength = round(hopDuration*fs);
    
    spec = melSpectrogram(x,fs, ...
        Window=hamming(frameLength,"periodic"), ...
        OverlapLength=frameLength-hopLength, ...
        FFTLength=2048, ...
        NumBands=numBands, ...
        FrequencyRange=[50,4000]);
    
    % If the spectrogram is less wide than numHops, then put spectrogram in
    % the middle of X.
    w = size(spec,2);
    left = floor((numHops-w)/2)+1;
    ind = left:left+w-1;
    X(:,ind,1,i) = spec;
    
    if mod(i,500) == 0
        disp("Processed " + i + " files out of " + numFiles)
    end
    
end

disp("...done");

end

%--------------------------------------------------------------------------
function x = normalizeAndResize(x)
% This function is only for use in the 
% "Spoken Digit Recognition with Wavelet Scattering and Deep Learning"
% example. It may change or be removed in a future release.

N = numel(x);
if N > 8192
    x = x(1:8192);
elseif N < 8192
    pad = 8192-N;
    prepad = floor(pad/2);
    postpad = ceil(pad/2);
    x = [zeros(prepad,1) ; x ; zeros(postpad,1)];
end
x = x./max(abs(x));
end