transform

Specify the file path to the audio samples included with Audio Toolbox™. Create an audio datastore that points to the specified folder.

folder = fullfile(matlabroot,'toolbox','audio','samples');
ADS = audioDatastore(folder);

Call transform to create a new datastore that mixes multichannel signals to mono.

ADSnew = transform(ADS,@(x)mean(x,2));

Read from the new datastore and confirm that it only outputs mono signals.

while hasdata(ADSnew)
    audio = read(ADSnew);
    fprintf('Number of channels = %d\n',size(audio,2))
end

Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1
Number of channels = 1

The audio samples included with Audio Toolbox™ have varying durations. Use the transform function to customize the read function so that it outputs a random five second segment of the audio samples.

Specify the file path to the audio samples included with Audio Toolbox. Create an audio datastore that points to the specified folder.

folder = fullfile(matlabroot,'toolbox','audio','samples');
ADS = audioDatastore(folder);

Define a function to take as input the output of the read function. Make the function extract five seconds worth of data from the audio signal.

function [dataOut,info] = extractSegment(audioIn,info)
    [N,numChan] = size(audioIn);
    newN = round(info.SampleRate*5);
    if newN > N                                % signal length < 5 seconds
        numPad = newN - N + 1;
        dataOut = [audioIn;zeros(numPad,numChan,'like',audioIn)];
    elseif newN < N                            % signal length > 5 seconds
        start = randi(N - newN + 1);
        dataOut = audioIn(start:start+newN-1,:);
    else                                       % signal length == 5 seconds 
        dataOut = audioIn;
    end
end

Call transform to create a TransformedDatastore with Transforms set to the function you defined.

ADSnew = transform(ADS,@extractSegment,'IncludeInfo',true)

ADSnew = 
  TransformedDatastore with properties:

      UnderlyingDatastores: {audioDatastore}
    SupportedOutputFormats: ["txt"    "csv"    "dat"    "asc"    "xlsx"    "xls"    "parquet"    "parq"    "png"    "jpg"    "jpeg"    "tif"    "tiff"    "wav"    "flac"    "ogg"    "opus"    "mp3"    "mp4"    "m4a"]
                Transforms: {[@extractSegment]}
               IncludeInfo: 1

Read the first three audio files and verify that the outputs are five second segments.

for i = 1:3
    [audio,info] = read(ADSnew);
    fprintf('Duration = %d seconds\n',size(audio,1)/info.SampleRate)
end

Duration = 5 seconds
Duration = 5 seconds
Duration = 5 seconds

Use transform to create an audio datastore that returns a mel spectrogram representation from the read function.

Specify the file path to the audio samples included with Audio Toolbox™. Create an audio datastore that points to the specified folder.

folder = fullfile(matlabroot,'toolbox','audio','samples');
ADS = audioDatastore(folder);

Define a function that transforms audio data from a time-domain representation to a log mel spectrogram. The function adds the additional outputs from the melSpectrogram function to the info struct output from reading the audio datastore.

function [dataOut,infoOut] = extractMelSpectrogram(audioIn,info)

    [S,F,T] = melSpectrogram(audioIn,info.SampleRate);
    
    dataOut = 10*log10(S+eps);
    infoOut = info;
    infoOut.CenterFrequencies = F;
    infoOut.TimeInstants = T;
end

Call transform to create a TransformedDatastore with Transforms set to extractMelSpectrogram.

ADSnew = transform(ADS,@extractMelSpectrogram,'IncludeInfo',true)

ADSnew = 
  TransformedDatastore with properties:

      UnderlyingDatastores: {audioDatastore}
    SupportedOutputFormats: ["txt"    "csv"    "dat"    "asc"    "xlsx"    "xls"    "parquet"    "parq"    "png"    "jpg"    "jpeg"    "tif"    "tiff"    "wav"    "flac"    "ogg"    "opus"    "mp3"    "mp4"    "m4a"]
                Transforms: {[@extractMelSpectrogram]}
               IncludeInfo: 1

Read the first three audio files and plot the log mel spectrograms. If there are multiple channels, plot only the first channel.

for i = 1:3
    [melSpec,info] = read(ADSnew);
    
    figure(i)
    surf(info.TimeInstants,info.CenterFrequencies,melSpec(:,:,1),'EdgeColor','none');
    xlabel('Time (s)')
    ylabel('Frequency (Hz)')
    [~,name] = fileparts(info.FileName);
    title(name)
    axis([0 info.TimeInstants(end) info.CenterFrequencies(1) info.CenterFrequencies(end)])
    view([0,90])
end

Use transform to create an audio datastore that returns feature vectors.

folder = fullfile(matlabroot,'toolbox','audio','samples');
ADS = audioDatastore(folder);

function [dataOut,info] = extractFeatureVector(audioIn,info)

    % Convert to frequency-domain representation
    windowLength = 256;
    overlapLength = 128;
    [~,f,~,S] = spectrogram(mean(audioIn,2), ...
                            hann(windowLength,"Periodic"), ...
                            overlapLength, ...
                            windowLength, ...
                            info.SampleRate, ...
                            "power", ...
                            "onesided");

    % Extract features
    [kurtosis,spread,centroid] = spectralKurtosis(S,f);
    skewness = spectralSkewness(S,f);
    crest    = spectralCrest(S,f);
    decrease = spectralDecrease(S,f);
    entropy  = spectralEntropy(S,f);
    flatness = spectralFlatness(S,f);
    flux     = spectralFlux(S,f);
    rolloff  = spectralRolloffPoint(S,f);
    slope    = spectralSlope(S,f);

    % Concatenate to create feature vectors
    dataOut = [kurtosis,spread,centroid,skewness,crest,decrease,entropy,flatness,flux,rolloff,slope];

end

ADSnew = transform(ADS,@extractFeatureVector,'IncludeInfo',true)

ADSnew = 

  TransformedDatastore with properties:

      UnderlyingDatastores: {audioDatastore}
    SupportedOutputFormats: ["txt"    "csv"    "dat"    ...    ] (1x20 string)
                Transforms: {[@extractFeatureVector]}
               IncludeInfo: 1

featureMatrix = read(ADSnew);
[numFeatureVectors,numFeatures] = size(featureMatrix)

numFeatureVectors =

        4215


numFeatures =

    11

Use transform to create an audio datastore that applies bandpass filtering before returning audio from the read function.

Specify the file path to the audio samples included with Audio Toolbox™. Create an audio datastore that points to the specified folder.

folder = fullfile(matlabroot,'toolbox','audio','samples');
ADS = audioDatastore(folder);

Define a function, applyBandpassFilter, that applies a bandpass filter with a passband between 1 and 15 kHz.

function [audioOut,info] = applyBandpassFilter(audioIn,info)

    audioOut = bandpass(audioIn,[1e3,15e3],info.SampleRate);

end

Call transform to create a TransformedDatastore with Transforms set to applyBandpassFilter.

ADSnew = transform(ADS,@applyBandpassFilter,'IncludeInfo',true)

ADSnew = 
  TransformedDatastore with properties:

      UnderlyingDatastores: {audioDatastore}
    SupportedOutputFormats: ["txt"    "csv"    "dat"    "asc"    "xlsx"    "xls"    "parquet"    "parq"    "png"    "jpg"    "jpeg"    "tif"    "tiff"    "wav"    "flac"    "ogg"    "opus"    "mp3"    "mp4"    "m4a"]
                Transforms: {[@applyBandpassFilter]}
               IncludeInfo: 1

Call read to return the bandpass filtered audio from the transform datastore. Call read to return the bandpass filtered audio from the original datastore. Plot the spectrograms to visualize the difference.

[audio1,info1] = read(ADS);
[audio2,info2] = read(ADSnew);

spectrogram(audio1,hann(512),256,512,info1.SampleRate,'yaxis')
title('Original Signal')

spectrogram(audio2,hann(512),256,512,info2.SampleRate,'yaxis')
title('Filtered Signal')