generateMATLABFunction

Create MATLAB function compatible with C/C++ code generation

Since R2020b

Syntax

generateMATLABFunction(afe)

generateMATLABFunction(afe,fileName)

generateMATLABFunction(___,'IsStreaming',TF)

Description

Generate Equivalent MATLAB Function

generateMATLABFunction(afe) generates code and opens an untitled file containing a function named extractAudioFeatures. The generated MATLAB^® function has the signature:

featureVector = extractAudioFeatures(audioIn)

The signature is equivalent to:

featureVector = extract(afe,audioIn)

generateMATLABFunction(afe,fileName) generates code and saves the resulting function to the file specified by fileName. The generated MATLAB function has the signature:

featureVector = functionName(audioIn)

The signature is equivalent to:

featureVector = extract(afe,audioIn)

Generate MATLAB Function for Stream Processing

generateMATLABFunction(___,'IsStreaming',TF) specifies whether the function is intended for stream (single-frame) processing. If TF is specified as true, the resulting function requires single-frame input of length numel(afe.Window). If individual feature extractors have state, the resulting function maintains the state between calls. If unspecified, TF defaults to false. The streaming function has the signature:

featureVector = functionName(audioIn,varargin)

The size of featureVector depends on the value of IsStreaming.

• If IsStreaming was set to true, then featureVector is returned as an M-by-N matrix, where M is the number of features extracted and N is the number of channels.

• If IsStreaming was set to false, then featureVector is returned as an L-by-M-by-N array, where L is the number of hops, M is the number of feature vectors, and N is the number of channels.

The possible values of varargin depends on the configuration of your audioFeatureExtractor object, afe.

• If the features your audioFeatureExtractor object extracts do not require state, then varargin must be empty.

• If the features your audioFeatureExtractor object extracts require state, then varargin can be the optional name-value pair 'Reset' and either true or false. If you call the function with 'Reset' set to true, then the function clears any state before calculating and returning the feature vector.

Examples

Generate Equivalent MATLAB® Function

You can use the audioFeatureExtractor object while developing a feature extraction pipeline in MATLAB. Once you are ready to deploy your system to a device or integrate it into a larger system, use generateMATLABFunction to create a MATLAB function suitable for C/C++ code generation. Then use MATLAB Coder™ to generate equivalent C/C++ code.

Read in an audio file. You will use this audio file to verify the equivalency of the audioFeatureExtractor object and the generated MATLAB function.

[audioIn,fs] = audioread('Counting-16-44p1-mono-15secs.wav');

Create an audioFeatureExtractor object to extract the Bark spectrum, the delta gammatone cepstral coefficients (GTCC), and the harmonic ratio.

afe = audioFeatureExtractor("Window",hann(512,"periodic"), ...
    "OverlapLength",256, ...
    "SampleRate",fs, ...
    "FFTLength",1024, ...
    'barkSpectrum',true, ...
    "gtccDelta",true, ...
    "harmonicRatio",true)

afe = 
  audioFeatureExtractor with properties:

   Properties
                     Window: [512x1 double]
              OverlapLength: 256
                 SampleRate: 44100
                  FFTLength: 1024
    SpectralDescriptorInput: 'linearSpectrum'
        FeatureVectorLength: 46

   Enabled Features
     barkSpectrum, gtccDelta, harmonicRatio

   Disabled Features
     linearSpectrum, melSpectrum, erbSpectrum, mfcc, mfccDelta, mfccDeltaDelta
     gtcc, gtccDeltaDelta, spectralCentroid, spectralCrest, spectralDecrease, spectralEntropy
     spectralFlatness, spectralFlux, spectralKurtosis, spectralRolloffPoint, spectralSkewness, spectralSlope
     spectralSpread, pitch, zerocrossrate, shortTimeEnergy


   To extract a feature, set the corresponding property to true.
   For example, obj.mfcc = true, adds mfcc to the list of enabled features.

Call generateMATLABFunction on the object and specify a name for the generated MATLAB function.

functionName = 'extractAudioFeatures';
generateMATLABFunction(afe,functionName)

The generated function is saved to your current folder.

type extractAudioFeatures

function featureVector = extractAudioFeatures(x)
%extractAudioFeatures Extract multiple features from batch audio
%   featureVector = extractAudioFeatures(audioIn) returns audio features
%   extracted from audioIn.
%
%   Parameters of the audioFeatureExtractor used to generate this
%   function must be honored when calling this function.
%    - Sample rate of the input should be 44100 Hz.
%    - Input frame length should be greater than or equal to 512 samples.
%
%
%      % Example 1: Extract features
%        source = dsp.ColoredNoise(SamplesPerFrame=44100);
%        for ii = 1:10
%            audioIn = source();
%            featureArray = extractAudioFeatures(audioIn);
%            % ... do something with featureArray ...
%        end
%
%
%      % Example 2: Generate code
%        targetDataType = "single";
%        codegen extractAudioFeatures -args {ones(44100,1,targetDataType)}
%        source = dsp.ColoredNoise(SamplesPerFrame=44100, ...
%                                  OutputDataType=targetDataType);
%        for ii = 1:10
%            audioIn = source();
%            featureArray = extractAudioFeatures_mex(audioIn);
%            % ... do something with featureArray ...
%        end
%
%   See also audioFeatureExtractor, dsp.AsyncBuffer, codegen.

%   Generated by audioFeatureExtractor on 05-Sep-2024 23:29:49 UTC-04:00
%#codegen

dataType = underlyingType(x);
[numSamples,numChannels] = size(x);

props = coder.const(getProps(dataType));

persistent config outputIndex
if isempty(outputIndex)
    [config, outputIndex] = coder.const(@getConfig,dataType,props);
end

% Preallocate feature vector
numHops = floor((numSamples-numel(props.Window))/(numel(props.Window) - props.OverlapLength)) + 1;
featureVector = coder.nullcopy(zeros(numHops,props.NumFeatures,numChannels,dataType));

% Short-time Fourier transform
Y = stft(x,Window=props.Window,OverlapLength=props.OverlapLength,FFTLength=props.FFTLength,FrequencyRange="onesided");
Z = reshape(Y,[],numHops*numChannels);
Zpower = real(Z.*conj(Z));

% Bark spectrum
y = applyFilterBank(config.barkSpectrum.FilterBank,Zpower,dataType);
barkSpectrum = reshape(y,[],numHops,numChannels);
featureVector(:,outputIndex.barkSpectrum,:) = permute(barkSpectrum,[2,1,3]);

% ERB spectrum
y = applyFilterBank(config.erbSpectrum.FilterBank,Zpower,dataType);
erbSpectrum = reshape(y,[],numHops,numChannels);

% Gammatone-frequency cepstral coefficients (GTCC)
gammacc = cepstralCoefficients(erbSpectrum,NumCoeffs=13,Rectification="log");
featureVector(:,outputIndex.gtccDelta,:) = audioDelta(gammacc,9);

% Periodicity features
featureVector(:,outputIndex.harmonicRatio,:) = harmonicRatio(x,props.SampleRate,Window=props.Window,OverlapLength=props.OverlapLength);
end

function props = getProps(dataType)
props.Window = 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= cast(256,dataType);
props.SampleRate = cast(44100,dataType);
props.FFTLength = uint16(1024);
props.NumFeatures = uint8(46);
end

function [config, outputIndex] = getConfig(dataType, props)
powerNormalizationFactor = 1/(sum(props.Window)^2);

barkFilterbank = designAuditoryFilterBank(props.SampleRate, ...
    FrequencyScale="bark", ...
    FFTLength=props.FFTLength, ...
    OneSided=true, ...
    FrequencyRange=[0 22050], ...
    NumBands=32, ...
    Normalization="bandwidth", ...
    FilterBankDesignDomain="linear");
barkFilterbank = barkFilterbank*powerNormalizationFactor;
config.barkSpectrum.FilterBank = cast(barkFilterbank,dataType);

erbFilterbank = coder.const(@feval,'designAuditoryFilterBank',props.SampleRate, ...
    FrequencyScale="erb", ...
    FFTLength=props.FFTLength, ...
    OneSided=true, ...
    FrequencyRange=[0 22050], ...
    NumBands=43, ...
    Normalization="bandwidth");
erbFilterbank = erbFilterbank*powerNormalizationFactor;
config.erbSpectrum.FilterBank = cast(erbFilterbank,dataType);

outputIndex.barkSpectrum = uint8(1:32);
outputIndex.gtccDelta = uint8(33:45);
outputIndex.harmonicRatio = uint8(46);
end

function y = applyFilterBank(filterBank, Z, dataType, varargin)
if isempty(coder.target)
    y = filterBank*Z;
else
    % Generate optimized C/C++ code for filter bank operation
    [numBands,filterLength] = size(filterBank);
    numChan = size(Z,2);

    if nargin == 3
        y = zeros(numBands,numChan,dataType);
    else
        y = varargin{1};
    end

    for channel = 1:numChan
        temp = zeros(numBands,1,dataType);
        for jj = 1:filterLength
            temp = temp + Z(jj,channel)*filterBank(:,jj);
        end
        y(:,channel) = temp;
    end
end
end

Calling the generated function is equivalent to calling extract on the audioFeatureExtrator object. You can replace calls to extract with calls to the generated function in your code. Verify the equivalency between the object and the function.

a = extract(afe,audioIn);
b = extractAudioFeatures(audioIn);
isequal(a,b)

ans = logical
   1

The generated function contains help text that indicates any requirements on the input. In this example, the only requirement is that the input sample rate should be 44.1 kHz. The help text also contains custom examples. These examples show how to use the function directly in MATLAB and how to generate C/C++ code.

help extractAudioFeatures

 extractAudioFeatures Extract multiple features from batch audio
    featureVector = extractAudioFeatures(audioIn) returns audio features
    extracted from audioIn.
 
    Parameters of the audioFeatureExtractor used to generate this
    function must be honored when calling this function.
     - Sample rate of the input should be 44100 Hz.
     - Input frame length should be greater than or equal to 512 samples.
 
 
       % Example 1: Extract features
         source = dsp.ColoredNoise(SamplesPerFrame=44100);
         for ii = 1:10
             audioIn = source();
             featureArray = extractAudioFeatures(audioIn);
             % ... do something with featureArray ...
         end
 
 
       % Example 2: Generate code
         targetDataType = "single";
         codegen extractAudioFeatures -args {ones(44100,1,targetDataType)}
         source = dsp.ColoredNoise(SamplesPerFrame=44100, ...
                                   OutputDataType=targetDataType);
         for ii = 1:10
             audioIn = source();
             featureArray = extractAudioFeatures_mex(audioIn);
             % ... do something with featureArray ...
         end
 
    See also audioFeatureExtractor, dsp.AsyncBuffer, codegen.

Run the first example to see how to use the function to extract features in MATLAB.

source = dsp.ColoredNoise("SamplesPerFrame",44100);
for ii = 1:10
    audioIn = source();
    featureArray = extractAudioFeatures(audioIn);
    % ... do something with featureArray ...
end

Run the second example to see how to generate a MATLAB executable from the function. Then use the MEX file to extract features while working in MATLAB. MATLAB Coder™ is required to run the following code.

targetDataType = "single";
codegen extractAudioFeatures -args {ones(44100,1,targetDataType)}

Code generation successful.

source = dsp.ColoredNoise("SamplesPerFrame",44100, ...
                          "OutputDataType",targetDataType);
for ii = 1:10
    audioIn = source();
    featureArray = extractAudioFeatures_mex(audioIn);
    % ... do something with featureArray ...
end

Generate MATLAB® Function for Stream Processing

You can use the audioFeatureExtractor object to develop a feature extraction pipeline in MATLAB. The audioFeatureExtractor is optimized to extract features from audio signals that contain several windows of data. Typically, audio features are extracted on time scales from 5 ms to 100 ms, depending on the application. When you are ready to deploy your system to a device or to integrate it into a larger system, you can use generateMATLABFunction to create a MATLAB function suitable for C/C++ code generation. Deployed systems are often concerned with minimizing latency. You can set the IsStreaming parameter to true when calling generateMATLABFunction to generate a MATLAB function that is optimized for stream processing. The generated MATLAB function assumes that the input has already been buffered and requires a fixed input frame size. The generated MATLAB function also maintains any required state for you between calls.

Read in an audio file. You will use this audio file to verify the approximate equivalency of the audioFeatureExtractor object and the generated MATLAB function.

[audioToVerify,fs] = audioread('Counting-16-44p1-mono-15secs.wav');

Create an audioFeatureExtractor object to extract the mel frequency cepstral coefficients (MFCC), the delta and delta-delta MFCC, the spectral centroid, and the pitch. Extract features from 30 ms windows with 20 ms overlap between windows.

afe = audioFeatureExtractor("Window",hann(round(0.03*fs),'periodic'), ...
    "OverlapLength",round(0.02*fs), ...
    "SampleRate",fs, ...
    "mfcc",true, ...
    "mfccDelta",true, ...
    "mfccDeltaDelta",true, ...
    "spectralCentroid",true, ...
    "pitch",true)

afe = 
  audioFeatureExtractor with properties:

   Properties
                     Window: [1323x1 double]
              OverlapLength: 882
                 SampleRate: 44100
                  FFTLength: []
    SpectralDescriptorInput: 'linearSpectrum'
        FeatureVectorLength: 41

   Enabled Features
     mfcc, mfccDelta, mfccDeltaDelta, spectralCentroid, pitch

   Disabled Features
     linearSpectrum, melSpectrum, barkSpectrum, erbSpectrum, gtcc, gtccDelta
     gtccDeltaDelta, spectralCrest, spectralDecrease, spectralEntropy, spectralFlatness, spectralFlux
     spectralKurtosis, spectralRolloffPoint, spectralSkewness, spectralSlope, spectralSpread, harmonicRatio
     zerocrossrate, shortTimeEnergy


   To extract a feature, set the corresponding property to true.
   For example, obj.mfcc = true, adds mfcc to the list of enabled features.

Call generateMATLABFunction on the object and specify a name and the full path for the generated MATLAB function. Set IsStreaming to true to generate a MATLAB function optimized for stream processing.

filename = fullfile(tempdir,"extractAudioFeatures");
generateMATLABFunction(afe,filename,'IsStreaming',true);

The generated function is saved to the tempdir folder. Because the mfccDelta and mfccDeltaDelta features require state, the generated function includes the ability to reset states using the optional name-value pair "Reset" and either true or false. If you generate a function that does not require state, the "Reset" parameter is not included in the generated function.

cd(tempdir)
type extractAudioFeatures

function featureVector = extractAudioFeatures(x, varargin)
%extractAudioFeatures Extract multiple features from streaming audio
%   featureVector = extractAudioFeatures(audioIn) returns audio features
%   extracted from audioIn.
%
%   featureVector = extractAudioFeatures(audioIn,Reset=TF) returns feature extractors
%   to their initial conditions before extracting features.
%
%   Parameters of the audioFeatureExtractor used to generate this
%   function must be honored when calling this function.
%    - Sample rate of the input should be 44100 Hz.
%    - Frame length of the input should be 1323 samples.
%    - Successive frames of the input should be overlapped by
%      882 samples before calling extractAudioFeatures.
%
%
%      % Example 1: Extract features
%        source = dsp.ColoredNoise();
%        inputBuffer = dsp.AsyncBuffer;
%        for ii = 1:10
%            audioIn = source();
%            write(inputBuffer,audioIn);
%            while inputBuffer.NumUnreadSamples > 441
%                x = read(inputBuffer,1323,882);
%                featureVector = extractAudioFeatures(x);
%                % ... do something with featureVector ...
%            end
%         end
%
%
%      % Example 2: Extract features from speech regions only
%        [audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav");
%        audioIn = resample(audioIn,44100,fs);
%        source = dsp.AsyncBuffer(size(audioIn,1));
%        write(source,audioIn);
%        TF = false;
%        while source.NumUnreadSamples > 441
%            x = read(source,1323,882);
%            isSilence = var(x) < 0.01;
%            if ~isSilence
%                featureVector = extractAudioFeatures(x,Reset=TF);
%                TF = false;
%            else
%                TF = true;
%            end
%            % ... do something with featureVector ...
%        end
%
%
%      % Example 3: Generate code that does not use reset
%        targetDataType = "single";
%        codegen extractAudioFeatures -args {ones(1323,1,targetDataType)}
%        source = dsp.ColoredNoise(OutputDataType=targetDataType);
%        inputBuffer = dsp.AsyncBuffer;
%        for ii = 1:10
%            audioIn = source();
%            write(inputBuffer,audioIn);
%            while inputBuffer.NumUnreadSamples > 441
%                x = read(inputBuffer,1323,882);
%                featureVector = extractAudioFeatures_mex(x);
%                % ... do something with featureVector ...
%            end
%         end
%
%
%      % Example 4: Generate code that uses reset
%        targetDataType = "single";
%        codegen extractAudioFeatures -args {ones(1323,1,targetDataType),"Reset",true}
%        [audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav");
%        audioIn = resample(audioIn,44100,fs);
%        source = dsp.AsyncBuffer(size(audioIn,1));
%        write(source,cast(audioIn,targetDataType));
%        TF = false;
%        while source.NumUnreadSamples > 441
%            x = read(source,1323,882);
%            isSilence = var(x) < 0.01;
%            if ~isSilence
%                featureVector = extractAudioFeatures_mex(x,Reset=TF);
%                TF = false;
%            else
%                TF = true;
%            end
%            % ... do something with featureVector ...
%        end
%
%   See also audioFeatureExtractor, dsp.AsyncBuffer, codegen.

%   Generated by audioFeatureExtractor on 05-Sep-2024 23:30:22 UTC-04:00
%#codegen

dataType = underlyingType(x);
numChannels = size(x,2);

props = coder.const(getProps(dataType));

persistent config outputIndex state
if isempty(outputIndex)
    [config, outputIndex] = coder.const(@getConfig,dataType,props);
    state = getState(dataType,numChannels);
else
    assert(state.NumChannels == numChannels)
end
if nargin==3
    if strcmpi(varargin{1},"Reset") && varargin{2}
        state = reset(state);
    end
end

% Preallocate feature vector
featureVector = coder.nullcopy(zeros(props.NumFeatures,numChannels,dataType));

% Fourier transform
Y = fft(bsxfun(@times,x,props.Window),props.FFTLength);
Z = Y(config.OneSidedSpectrumBins,:);
Zpower = real(Z.*conj(Z));

% Linear spectrum
linearSpectrum = Zpower(config.linearSpectrum.FrequencyBins,:)*config.linearSpectrum.NormalizationFactor;
linearSpectrum(1,:) = 0.5*linearSpectrum(1,:);
linearSpectrum = reshape(linearSpectrum,[],1,numChannels);

% Mel spectrum
y = applyFilterBank(config.melSpectrum.FilterBank,Zpower,dataType);
melSpectrum = reshape(y,[],1,numChannels);

% Mel-frequency cepstral coefficients (MFCC)
melcc = cepstralCoefficients(melSpectrum,NumCoeffs=13,Rectification="log");
featureVector(outputIndex.mfcc,:) = melcc;
[melccDelta,state.mfccDelta] = audioDelta(melcc,9,state.mfccDelta);
featureVector(outputIndex.mfccDelta,:) = melccDelta;
[featureVector(outputIndex.mfccDeltaDelta,:),state.mfccDeltaDelta] = audioDelta(melccDelta,9,state.mfccDeltaDelta);

% Spectral descriptors
featureVector(outputIndex.spectralCentroid,:) = spectralCentroid(linearSpectrum,config.SpectralDescriptorInput.FrequencyVector);

% Periodicity features
featureVector(outputIndex.pitch,:) = pitch(x,props.SampleRate,WindowLength=numel(props.Window),OverlapLength=props.OverlapLength,Method='NCF',Range=coder.const(cast([50 400],like=x)));
end

function props = getProps(dataType)
props.Window = 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= cast(882,dataType);
props.SampleRate = cast(44100,dataType);
props.FFTLength = uint16(1323);
props.NumFeatures = uint8(41);
end

function [config, outputIndex] = getConfig(dataType, props)
powerNormalizationFactor = 1/(sum(props.Window)^2);

config.OneSidedSpectrumBins = uint16(1:662);

linearSpectrumFrequencyBins = 1:662;
config.linearSpectrum.FrequencyBins = uint16(linearSpectrumFrequencyBins);
config.linearSpectrum.NormalizationFactor = cast(2*powerNormalizationFactor,dataType);

melFilterbank = designAuditoryFilterBank(props.SampleRate, ...
    FrequencyScale="mel", ...
    MelStyle="oshaughnessy", ...
    FFTLength=props.FFTLength, ...
    OneSided=true, ...
    FrequencyRange=[0 22050], ...
    NumBands=32, ...
    Normalization="bandwidth", ...
    FilterBankDesignDomain="linear");
melFilterbank = melFilterbank*powerNormalizationFactor;
config.melSpectrum.FilterBank = cast(melFilterbank,dataType);

FFTLength = cast(props.FFTLength,like=props.SampleRate);
w = (props.SampleRate/FFTLength)*(linearSpectrumFrequencyBins-1);
w(end) = props.SampleRate*(FFTLength-1)/(2*FFTLength);
config.SpectralDescriptorInput.FrequencyVector = cast(w(:),dataType);

outputIndex.mfcc = uint8(1:13);
outputIndex.mfccDelta = uint8(14:26);
outputIndex.mfccDeltaDelta = uint8(27:39);
outputIndex.spectralCentroid = uint8(40);
outputIndex.pitch = uint8(41);
end

function state = getState(dataType, numChannels)
state.NumChannels = numChannels;
state.mfccDelta = zeros(8,13,numChannels,dataType);
state.mfccDeltaDelta = zeros(8,13,numChannels,dataType);
end

function state = reset(state)
state.mfccDelta(:,:,:) = 0;
state.mfccDeltaDelta(:,:,:) = 0;
end

function y = applyFilterBank(filterBank, Z, dataType, varargin)
if isempty(coder.target)
    y = filterBank*Z;
else
    % Generate optimized C/C++ code for filter bank operation
    [numBands,filterLength] = size(filterBank);
    numChan = size(Z,2);

    if nargin == 3
        y = zeros(numBands,numChan,dataType);
    else
        y = varargin{1};
    end

    for channel = 1:numChan
        temp = zeros(numBands,1,dataType);
        for jj = 1:filterLength
            temp = temp + Z(jj,channel)*filterBank(:,jj);
        end
        y(:,channel) = temp;
    end
end
end

The generated function contains help text that indicates any requirements on the input. In this example, the sample rate of the input should be 44.1 kHz, the frame input to the function should be 1323 samples, and successive frames should be overlapped by 882 samples before calling the function. The differences between the audioFeatureExtractor object and the function are described in more detail in Approximate Equivalency Between audioFeatureExtractor and Generated Function.

help extractAudioFeatures

 extractAudioFeatures Extract multiple features from streaming audio
    featureVector = extractAudioFeatures(audioIn) returns audio features
    extracted from audioIn.
 
    featureVector = extractAudioFeatures(audioIn,Reset=TF) returns feature extractors
    to their initial conditions before extracting features.
 
    Parameters of the audioFeatureExtractor used to generate this
    function must be honored when calling this function.
     - Sample rate of the input should be 44100 Hz.
     - Frame length of the input should be 1323 samples.
     - Successive frames of the input should be overlapped by
       882 samples before calling extractAudioFeatures.
 
 
       % Example 1: Extract features
         source = dsp.ColoredNoise();
         inputBuffer = dsp.AsyncBuffer;
         for ii = 1:10
             audioIn = source();
             write(inputBuffer,audioIn);
             while inputBuffer.NumUnreadSamples > 441
                 x = read(inputBuffer,1323,882);
                 featureVector = extractAudioFeatures(x);
                 % ... do something with featureVector ...
             end
          end
 
 
       % Example 2: Extract features from speech regions only
         [audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav");
         audioIn = resample(audioIn,44100,fs);
         source = dsp.AsyncBuffer(size(audioIn,1));
         write(source,audioIn);
         TF = false;
         while source.NumUnreadSamples > 441
             x = read(source,1323,882);
             isSilence = var(x) < 0.01;
             if ~isSilence
                 featureVector = extractAudioFeatures(x,Reset=TF);
                 TF = false;
             else
                 TF = true;
             end
             % ... do something with featureVector ...
         end
 
 
       % Example 3: Generate code that does not use reset
         targetDataType = "single";
         codegen extractAudioFeatures -args {ones(1323,1,targetDataType)}
         source = dsp.ColoredNoise(OutputDataType=targetDataType);
         inputBuffer = dsp.AsyncBuffer;
         for ii = 1:10
             audioIn = source();
             write(inputBuffer,audioIn);
             while inputBuffer.NumUnreadSamples > 441
                 x = read(inputBuffer,1323,882);
                 featureVector = extractAudioFeatures_mex(x);
                 % ... do something with featureVector ...
             end
          end
 
 
       % Example 4: Generate code that uses reset
         targetDataType = "single";
         codegen extractAudioFeatures -args {ones(1323,1,targetDataType),"Reset",true}
         [audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav");
         audioIn = resample(audioIn,44100,fs);
         source = dsp.AsyncBuffer(size(audioIn,1));
         write(source,cast(audioIn,targetDataType));
         TF = false;
         while source.NumUnreadSamples > 441
             x = read(source,1323,882);
             isSilence = var(x) < 0.01;
             if ~isSilence
                 featureVector = extractAudioFeatures_mex(x,Reset=TF);
                 TF = false;
             else
                 TF = true;
             end
             % ... do something with featureVector ...
         end
 
    See also audioFeatureExtractor, dsp.AsyncBuffer, codegen.

The examples in the help show how to use the function directly in MATLAB and how to generate C/C++ code. Run the first example to see how to use the function to extract features in MATLAB.

source = dsp.ColoredNoise();
inputBuffer = dsp.AsyncBuffer;
for ii = 1:10
    audioIn = source();
    write(inputBuffer,audioIn);
    while inputBuffer.NumUnreadSamples > 441
        x = read(inputBuffer,1323,882);
        featureVector = extractAudioFeatures(x);
        % ... do something with featureVector ...
    end
end

Run the second example to see how to extract features in MATLAB while using the optional "Reset" name-value pair. The Reset name-value pair enables you to reset states on the function. For example, if you are only concerned with extracting features from regions of voiced speech and want to avoid the overhead of extracting features constantly, you can use the "Reset" parameter to avoid bleeding feature information between regions.

[audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav");
source = dsp.AsyncBuffer(size(audioIn,1));
write(source,audioIn);
TF = false;
while source.NumUnreadSamples > 441
    x = read(source,1323,882);
    isSilence = var(x) < 0.01;
    if ~isSilence
        featureVector = extractAudioFeatures(x,"Reset",TF);
        TF = false;
    else
        TF = true;
    end
    % ... do something with featureVector ...
end

Run the third example to see how to generate code that does not include the ability to reset state. When generating code that does not use the 'Reset' parameter, only specify a prototype for the audio input argument. The following code requires MATLAB Coder™.

targetDataType = "single";
codegen extractAudioFeatures -args {ones(1323,1,targetDataType)}

Code generation successful.

source = dsp.ColoredNoise('OutputDataType',targetDataType);
inputBuffer = dsp.AsyncBuffer;
for ii = 1:10
    audioIn = source();
    write(inputBuffer,audioIn);
    while inputBuffer.NumUnreadSamples > 441
        x = read(inputBuffer,1323,882);
        featureVector = extractAudioFeatures_mex(x);
        % ... do something with featureVector ...
    end
end

Run the fourth example to see how to generate code that can reset state. When generating code that uses the 'Reset' parameter, specify prototype input arguments for the full function signature. The following code requires MATLAB Coder™.

targetDataType = "single";
codegen extractAudioFeatures -args {ones(1323,1,targetDataType),'Reset',true}

Code generation successful.

[audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav");
source = dsp.AsyncBuffer(size(audioIn,1));
write(source,cast(audioIn,targetDataType));
TF = false;
while source.NumUnreadSamples > 441
    x = read(source,1323,882);
    isSilence = var(x) < 0.01;
    if ~isSilence
        featureVector = extractAudioFeatures_mex(x,'Reset',TF);
        TF = false;
    else
        TF = true;
    end
    % ... do something with featureVector ...
end

Approximate Equivalency Between audioFeatureExtractor and Generated Function

When you call extract using audioFeatureExtractor, the input is buffered internally prior to feature extraction. The output from extract is an L-by-M-by-N array, where L is the number of feature vectors and is equal to the number of analysis windows. M is the number of features extracted per analysis window. N is the number of channels.

featuresA = extract(afe,audioToVerify);
[L,M,N] = size(featuresA)

L = 
1551

M = 
41

N = 
1

When you call the generated function, extractAudioFeatures, the input should represent a single frame of audio data. The output from the generated function is an M-by-N matrix, where M is the number of features extracted and N is the number of channels. Use the generated function to extract features from the audio signal audioIn. Use the dsp.AsyncBuffer object to buffer the input into the required frame length and overlap length prior to calling extractAudioFeatures. Reshape the extracted feature vectors to match the orientation output from audioFeatureExtractor.

frameLength = 1323;
overlapLength = 882;
hopLength = frameLength - overlapLength;

featuresB = zeros(L,M,N);

buff = dsp.AsyncBuffer('Capacity',numel(audioToVerify));
write(buff,audioToVerify);

hop = 1;
while buff.NumUnreadSamples > hopLength
    if hop==1
        x = read(buff,frameLength);
        features = extractAudioFeatures(x,'Reset',true);
    else
        x = read(buff,frameLength,overlapLength);
        features = extractAudioFeatures(x);
    end
    featuresB(hop,:,:) = reshape(features,[1,M,N]);
    hop = hop + 1;
end

Visualize the difference between the output from audioFeatureExtractor and the generated function. The differences between frames are less than 1.8e-14 and are due to the different code paths being optimized for batch versus stream processing.

differenceBetweenFrames = sum(abs(featuresA-featuresB),2);
plot(differenceBetweenFrames)
xlabel('Frame')
title('Absolute Difference Between Feature Vectors')

Input Arguments

afe — Input object

audioFeatureExtractor object

Input object, specified as an audioFeatureExtractor object.

fileName — File name

character vector | string scalar

File name where the generated function is saved, specified as a character vector or string scalar.

Data Types: char | string

TF — Flag to specify if function is for streaming

false (default) | true

Flag to specify if generated function is intended for stream processing, specified as true or false.

Data Types: logical

Version History

Introduced in R2020b