Lucas asked . 2020-12-31

Speech Command detection in audio file

I need to remake "Speech Command Recognition Using Deep Learning" example so I can read audio from the wav file and get time intervals in which the command appears, but I don't know how to change real-time analysis from microphone into static file analysis in this example. Thank you for your help.

 %% Detect Commands Using Streaming Audio from Microphone
% Test your newly trained command detection network on streaming audio from
% your microphone. If you have not trained a network, then type
% |load('commandNet.mat')| at the command line to load a pretrained network
% and the parameters required to classify live, streaming audio. Try
% saying one of the commands, for example, _yes_, _no_, or _stop_.
% Then, try saying one of the unknown words such as _Marvin_, _Sheila_, _bed_,
% _house_, _cat_, _bird_, or any number from zero to nine.


%%
% Specify the audio sampling rate and classification rate in Hz and create
% an audio device reader that can read audio from your microphone.
fs = 16e3;
classificationRate = 20;
audioIn = audioDeviceReader('SampleRate',fs, ...
    'SamplesPerFrame',floor(fs/classificationRate));

%%
% Specify parameters for the streaming spectrogram computations and
% initialize a buffer for the audio. Extract the classification labels of
% the network. Initialize buffers of half a second for the labels and
% classification probabilities of the streaming audio. Use these buffers to
% compare the classification results over a longer period of time and by
% that build 'agreement' over when a command is detected.
frameLength = frameDuration*fs;
hopLength = hopDuration*fs;
waveBuffer = zeros([fs,1]);

labels = trainedNet.Layers(end).Classes;
YBuffer(1:classificationRate/2) = categorical("background");
probBuffer = zeros([numel(labels),classificationRate/2]);

framesNumber = audio/frameLength;

%%
% Create a figure and detect commands as long as the created figure exists.
% To stop the live detection, simply close the figure. 
h = figure('Units','normalized','Position',[0.2 0.1 0.6 0.8]);

while ishandle(h)
    
    % Extract audio samples from the audio device and add the samples to
    % the buffer.
    x = audioIn();
    waveBuffer(1:end-numel(x)) = waveBuffer(numel(x)+1:end);
    waveBuffer(end-numel(x)+1:end) = x;
    
    % Compute the spectrogram of the latest audio samples.
    spec = auditorySpectrogram(waveBuffer,fs, ...
        'WindowLength',frameLength, ...
        'OverlapLength',frameLength-hopLength, ...
        'NumBands',numBands, ...
        'Range',[50,7000], ...
        'WindowType','Hann', ...
        'WarpType','Bark', ...
        'SumExponent',2);
    spec = log10(spec + epsil);
    
    % Classify the current spectrogram, save the label to the label buffer,
    % and save the predicted probabilities to the probability buffer.
    [YPredicted,probs] = classify(trainedNet,spec,'ExecutionEnvironment','cpu');
    YBuffer(1:end-1)= YBuffer(2:end);
    YBuffer(end) = YPredicted;
    probBuffer(:,1:end-1) = probBuffer(:,2:end);
    probBuffer(:,end) = probs';
    
    % Plot the current waveform and spectrogram.
    subplot(2,1,1);
    plot(waveBuffer)
    axis tight
    ylim([-0.2,0.2])
    
    subplot(2,1,2)
    pcolor(spec)
    caxis([specMin+2 specMax])
    shading flat
    
    % Now do the actual command detection by performing a very simple
    % thresholding operation. Declare a detection and display it in the
    % figure title if all of the following hold:
    % 1) The most common label is not |background|.
    % 2) At least |countThreshold| of the latest frame labels agree.
    % 3) The maximum predicted probability of the predicted label is at
    % least |probThreshold|. Otherwise, do not declare a detection.
    [YMode,count] = mode(YBuffer);
    countThreshold = ceil(classificationRate*0.2);
    maxProb = max(probBuffer(labels == YMode,:));
    probThreshold = 0.5;
    subplot(2,1,1);
    if YMode == "background" || count

matlab , signal , signal processing