load('network_Audio_SequentialFeatureSelection.mat','bestNet','afe','normalizers');

fs = afe.SampleRate;

deviceReader = audioDeviceReader('SampleRate',fs,'SamplesPerFrame',256);

audioBuffer = dsp.AsyncBuffer(fs*3);
steBuffer = dsp.AsyncBuffer(1000);
predictionBuffer = dsp.AsyncBuffer(5);

fig = figure;

streamAxes = subplot(3,1,1);
streamPlot = plot(zeros(fs,1));
ylabel('Amplitude')
xlabel('Time (s)')
title('Audio Stream')
streamAxes.XTick = [0,fs];
streamAxes.XTickLabel = [0,1];
streamAxes.YLim = [-1,1];

analyzedAxes = subplot(3,1,2);
analyzedPlot = plot(zeros(fs/2,1));
title('Analyzed Segment')
ylabel('Amplitude')
xlabel('Time (s)')
set(gca,'XTickLabel',[])
analyzedAxes.XTick = [0,fs/2];
analyzedAxes.XTickLabel = [0,0.5];
analyzedAxes.YLim = [-1,1];

probabilityAxes = subplot(3,1,3);
probabilityPlot = bar(0:9,0.1*ones(1,10));
axis([-1,10,0,1])
ylabel('Probability')
xlabel('Class')

steThreshold = 0.015;
idxVec = 1:fs;
tic
while toc < 20
    
    % Read in a frame of audio from your device.
    audioIn = deviceReader();
    
    % Write the audio into a the buffer.
    write(audioBuffer,audioIn);
    
    % While 200 ms of data is unused, continue this loop.
    while audioBuffer.NumUnreadSamples > 0.2*fs
        
        % Read 1 second from the audio buffer. Of that 1 second, 800 ms
        % is rereading old data and 200 ms is new data.
        audioToAnalyze = read(audioBuffer,fs,0.8*fs);
        
        % Update the figure to plot the current audio data.
        streamPlot.YData = audioToAnalyze;

        ste = mean(abs(audioToAnalyze));
        write(steBuffer,ste);
        if steBuffer.NumUnreadSamples > 5
            abc = sort(peek(steBuffer));
            steThreshold = abc(round(0.4*numel(abc)));
        end
        if ste > steThreshold
            
            % Use the detectSpeeech function to determine if a region of speech
            % is present.
            idx = detectSpeech(audioToAnalyze,fs);
            
            % If a region of speech is present, perform the following.
            if ~isempty(idx)
                % Zero out all parts of the signal except the speech
                % region, and trim to 0.5 seconds.
                audioToAnalyze = HelperTrimOrPad(audioToAnalyze(idx(1,1):idx(1,2)),fs/2);
                
                % Normalize the audio.
                audioToAnalyze = audioToAnalyze/max(abs(audioToAnalyze));
                
                % Update the analyzed segment plot
                analyzedPlot.YData = audioToAnalyze;

                % Extract the features and transpose them so that time is
                % across columns.
                features = (extract(afe,audioToAnalyze))';

                % Normalize the features.
                features = (features - normalizers.Mean) ./ normalizers.StandardDeviation;
                
                % Call classify to determine the probabilities and the
                % winning label.
                features(isnan(features)) = 0;
                [label,probs] = classify(bestNet,features);
                
                % Update the plot with the probabilities and the winning
                % label.
                probabilityPlot.YData = probs;
                write(predictionBuffer,probs);

                if predictionBuffer.NumUnreadSamples == predictionBuffer.Capacity
                    lastTen = peek(predictionBuffer);
                    [~,decision] = max(mean(lastTen.*hann(size(lastTen,1)),1));
                    probabilityAxes.Title.String = num2str(decision-1);
                end
            end
        else
            % If the signal energy is below the threshold, assume no speech
            % detected.
             probabilityAxes.Title.String = '';
             probabilityPlot.YData = 0.1*ones(10,1);
             analyzedPlot.YData = zeros(fs/2,1);
             reset(predictionBuffer)
        end
        
        drawnow limitrate
    end
end

url = "https://zenodo.org/record/1342401/files/Jakobovski/free-spoken-digit-dataset-v1.0.8.zip";
downloadFolder = tempdir;
datasetFolder = fullfile(downloadFolder,'FSDD');

if ~exist(datasetFolder,'dir')
    fprintf('Downloading Free Spoken Digit Dataset ...\n')
    unzip(url,datasetFolder)
end

ads = audioDatastore(datasetFolder,'IncludeSubfolders',true);
[~,adsInfo] = read(ads);
fs = adsInfo.SampleRate;

[~,filenames] = cellfun(@(x)fileparts(x),ads.Files,'UniformOutput',false);
ads.Labels = categorical(string(cellfun(@(x)x(1),filenames)));

[adsTrain,adsValidation] = splitEachLabel(ads,0.8);

win = hamming(round(0.03*fs),"periodic");
overlapLength = round(0.02*fs);

afe = audioFeatureExtractor( ...
    'Window',       win, ...
    'OverlapLength',overlapLength, ...
    'SampleRate',   fs, ...
    ...
    'linearSpectrum',      false, ...
    'melSpectrum',         false, ...
    'barkSpectrum',        false, ...
    'erbSpectrum',         false, ...
    ...
    'mfcc',                true, ...
    'mfccDelta',           true, ...
    'mfccDeltaDelta',      true, ...
    'gtcc',                true, ...
    'gtccDelta',           true, ...
    'gtccDeltaDelta',      true, ...
    ...
    'spectralCentroid',    true, ...
    'spectralCrest',       true, ...
    'spectralDecrease',    true, ...
    'spectralEntropy',     true, ...
    'spectralFlatness',    true, ...
    'spectralFlux',        true, ...
    'spectralKurtosis',    true, ...
    'spectralRolloffPoint',true, ...
    'spectralSkewness',    true, ...
    'spectralSlope',       true, ...
    'spectralSpread',      true, ...
    ...
    'pitch',               false, ...
    'harmonicRatio',       false);

numUnits = 100;
слои = [ ...
    sequenceInputLayer (1)
    bilstmLayer (numUnits,"OutputMode","last")
    fullyConnectedLayer (numel (категории (adsTrain. Метки)))
    softmaxLayer
    classificationLayer];

опции = trainingOptions ("adam", ...
    "LearnRateSchedule","piecewise", ...
    "Shuffle","every-epoch", ...
    "Verbose"ложь, ...
    "MaxEpochs",20);

direction = 'forward';
[вахтенный журнал, bestFeatures, bestNet, нормализаторы] = HelperSFS (adsTrain, adsValidation, afe, слои, опции, направление);

logbook

bestFeatures

set(afe,bestFeatures)
afe

% save('network_Audio_SequentialFeatureSelection.mat','bestNet','afe','normalizers')

function [trueLabels,predictedLabels,net,normalizers] = HelperTrainAndValidateNetwork(adsTrain,adsValidation,afe,layers,options)
% Train and validate a network.
%
%   INPUTS:
%   adsTrain      - audioDatastore object that points to training set
%   adsValidation - audioDatastore object that points to validation set
%   afe           - audioFeatureExtractor object.
%   layers        - Layers of LSTM or BiLSTM network
%   options       - traingOptions object
%
%   OUTPUTS:
%   trueLabels      - true labels of validation set
%   predictedLabels - predicted labels of validation set
%   net             - trained network
%   normalizers     - normalization factors for features under test

% Copyright 2019 The MathWorks, Inc.

% Convert the data to tall arrays.
tallTrain      = tall(adsTrain);
tallValidation = tall(adsValidation);

% Extract features from the training set. Reorient the features so that
% time is along rows to be compatible with sequenceInputLayer.
fs = afe.SampleRate;
tallTrain         = cellfun(@(x)HelperTrimOrPad(x,fs/2),tallTrain,"UniformOutput",false);
tallTrain         = cellfun(@(x)x/max(abs(x),[],'all'),tallTrain,"UniformOutput",false);
tallFeaturesTrain = cellfun(@(x)extract(afe,x),tallTrain,"UniformOutput",false);
tallFeaturesTrain = cellfun(@(x)x',tallFeaturesTrain,"UniformOutput",false);  %#ok<NASGU>
[~,featuresTrain] = evalc('gather(tallFeaturesTrain)'); % Use evalc to suppress command-line output.

tallValidation         = cellfun(@(x)HelperTrimOrPad(x,fs/2),tallValidation,"UniformOutput",false);
tallValidation         = cellfun(@(x)x/max(abs(x),[],'all'),tallValidation,"UniformOutput",false);
tallFeaturesValidation = cellfun(@(x)extract(afe,x),tallValidation,"UniformOutput",false);
tallFeaturesValidation = cellfun(@(x)x',tallFeaturesValidation,"UniformOutput",false); %#ok<NASGU>
[~,featuresValidation] = evalc('gather(tallFeaturesValidation)'); % Use evalc to suppress command-line output.

% Use the training set to determine the mean and standard deviation of each
% feature. Normalize the training and validation sets.
allFeatures = cat(2,featuresTrain{:});
M = mean(allFeatures,2,'omitnan');
S = std(allFeatures,0,2,'omitnan');
featuresTrain = cellfun(@(x)(x-M)./S,featuresTrain,'UniformOutput',false);
for ii = 1:numel(featuresTrain)
    idx = find(isnan(featuresTrain{ii}));
    if ~isempty(idx)
        featuresTrain{ii}(idx) = 0;
    end
end
featuresValidation = cellfun(@(x)(x-M)./S,featuresValidation,'UniformOutput',false);
for ii = 1:numel(featuresValidation)
    idx = find(isnan(featuresValidation{ii}));
    if ~isempty(idx)
        featuresValidation{ii}(idx) = 0;
    end
end

% Replicate the labels of the train and validation sets so that they are in
% one-to-one correspondence with the sequences.
labelsTrain = adsTrain.Labels;

% Update input layer for the number of features under test.
layers(1) = sequenceInputLayer(size(featuresTrain{1},1));

% Train the network.
net = trainNetwork(featuresTrain,labelsTrain,layers,options);

% Evaluate the network. Call classify to get the predicted labels for each
% sequence.
predictedLabels = classify(net,featuresValidation);
trueLabels = adsValidation.Labels;

% Save the normalization factors as a struct.
normalizers.Mean = M;
normalizers.StandardDeviation = S;
end

function [logbook,bestFeatures,bestNet,bestNormalizers] = HelperSFS(adsTrain,adsValidate,afeThis,layers,options,direction)
%
%   INPUTS:
%   adsTrain    - audioDatastore object that points to training set
%   adsValidate - audioDatastore object that points to validation set
%   afe         - audioFeatureExtractor object. Set all features to test to true
%   layers      - Layers of LSTM or BiLSTM network
%   options     - traingOptions object
%   direction   - SFS direction, specify as 'forward' or 'backward'
%
%   OUTPUTS:
%   logbook         - table containing feature configurations tested and corresponding validation accuracies
%   bestFeatures    - struct containg best feature configuration
%   bestNet         - Trained network with highest validation accuracy
%   bestNormalizers - Feature normalization factors for best features

% Copyright 2019 The MathWorks, Inc.

afe = copy(afeThis);
featuresToTest = fieldnames(info(afe));
N = numel(featuresToTest);
bestValidationAccuracy = 0;

% Set the initial feature configuration: all on for backward selection
% or all off for forward selection.
featureConfig = info(afe);
for i = 1:N
    if strcmpi(direction,"backward")
        featureConfig.(featuresToTest{i}) = true;
    else
        featureConfig.(featuresToTest{i}) = false;
    end
end

% Initialize logbook to track feature configuration and accuracy.
logbook = table(featureConfig,0,'VariableNames',["Feature Configuration","Accuracy"]);

% Perform sequential feature evaluation.
wrapperIdx = 1;
bestAccuracy = 0;
while wrapperIdx <= N
    % Create a cell array containing all feature configurations to test
    % in the current loop.
    featureConfigsToTest = cell(numel(featuresToTest),1);
    for ii = 1:numel(featuresToTest)
        if strcmpi(direction,"backward")
            featureConfig.(featuresToTest{ii}) = false;
        else
            featureConfig.(featuresToTest{ii}) = true;
        end
        featureConfigsToTest{ii} = featureConfig;
        if strcmpi(direction,"backward")
            featureConfig.(featuresToTest{ii}) = true;
        else
            featureConfig.(featuresToTest{ii}) = false;
        end
    end

    % Loop over every feature set.
    for ii = 1:numel(featureConfigsToTest)

        % Determine the current feature configuration to test. Update
        % the feature afe.
        currentConfig = featureConfigsToTest{ii};
        set(afe,currentConfig)

        % Train and get k-fold cross-validation accuracy for current
        % feature configuration.
        [trueLabels,predictedLabels,net,normalizers] = HelperTrainAndValidateNetwork(adsTrain,adsValidate,afe,layers,options);
        valAccuracy = mean(trueLabels==predictedLabels)*100;
        if valAccuracy > bestValidationAccuracy
            bestValidationAccuracy = valAccuracy;
            bestNet = net;
            bestNormalizers = normalizers;
        end

        % Update Logbook
        result = table(currentConfig,valAccuracy,'VariableNames',["Feature Configuration","Accuracy"]);
        logbook = [logbook;result]; %#ok<AGROW> 

    end

    % Determine and print the setting with the best accuracy. If accuracy
    % did not improve, end the run.
    [a,b] = max(logbook{:,'Accuracy'});
    if a <= bestAccuracy
        wrapperIdx = inf;
    else
        wrapperIdx = wrapperIdx + 1;
    end
    bestAccuracy = a;

    % Update the features-to-test based on the most recent winner.
    winner = logbook{b,'Feature Configuration'};
    fn = fieldnames(winner);
    tf = structfun(@(x)(x),winner);
    if strcmpi(direction,"backward")
        featuresToRemove = fn(~tf);
    else
        featuresToRemove = fn(tf);
    end
    for ii = 1:numel(featuresToRemove)
        loc =  strcmp(featuresToTest,featuresToRemove{ii});
        featuresToTest(loc) = [];
        if strcmpi(direction,"backward")
            featureConfig.(featuresToRemove{ii}) = false;
        else
            featureConfig.(featuresToRemove{ii}) = true;
        end
    end

end

% Sort the logbook and make it more readable.
logbook(1,:) = []; % Delete placeholder first row.
logbook = sortrows(logbook,{'Accuracy'},{'descend'});
bestFeatures = logbook{1,'Feature Configuration'};
m = logbook{:,'Feature Configuration'};
fn = fieldnames(m);
myString = strings(numel(m),1);
for wrapperIdx = 1:numel(m)
    tf = structfun(@(x)(x),logbook{wrapperIdx,'Feature Configuration'});
    myString(wrapperIdx) = strjoin(fn(tf),", ");
end
logbook = table(myString,logbook{:,'Accuracy'},'VariableNames',["Features","Accuracy"]);
end

function y = HelperTrimOrPad(x,n)
% y = HelperTrimOrPad(x,n) trims or pads the input x to n samples. If x is
% trimmed, it is trimmed equally on the front and back. If x is padded, it is
% padded equally on the front and back with zeros. For odd-length trimming or
% padding, the extra sample is trimmed or padded from the back.

% Copyright 2019 The MathWorks, Inc.
a = size(x,1);
if a < n
    frontPad = floor((n-a)/2);
    backPad = n - a - frontPad;
    y = [zeros(frontPad,1);x;zeros(backPad,1)];
elseif a > n
    frontTrim = floor((a-n)/2)+1;
    backTrim = a - n - frontTrim;
    y = x(frontTrim:end-backTrim);
else
    y = x;
end
end