extract
Extract audio features
Syntax
Description
features = extract(aFE,ds)audioDatastore
object ds.
features = extract(aFE,ds,Name=Value)extract(aFE,ds,UseParallel=true) reads the data and extracts features
in parallel.
Examples
Read in an audio signal.
[audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav");Create an audioFeatureExtractor to extract the centroid of the Bark spectrum, the kurtosis of the Bark spectrum, and the pitch of an audio signal.
aFE = audioFeatureExtractor("SampleRate",fs, ... "SpectralDescriptorInput","barkSpectrum", ... "spectralCentroid",true, ... "spectralKurtosis",true, ... "pitch",true)
aFE =
audioFeatureExtractor with properties:
Properties
Window: [1024×1 double]
OverlapLength: 512
SampleRate: 44100
FFTLength: []
SpectralDescriptorInput: 'barkSpectrum'
FeatureVectorLength: 3
Enabled Features
spectralCentroid, spectralKurtosis, pitch
Disabled Features
linearSpectrum, melSpectrum, barkSpectrum, erbSpectrum, mfcc, mfccDelta
mfccDeltaDelta, gtcc, gtccDelta, gtccDeltaDelta, spectralCrest, spectralDecrease
spectralEntropy, spectralFlatness, spectralFlux, 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 extract to extract the features from the audio signal. Normalize the features by their mean and standard deviation.
features = extract(aFE,audioIn); features = (features - mean(features,1))./std(features,[],1);
Plot the normalized features over time.
idx = info(aFE); duration = size(audioIn,1)/fs; subplot(2,1,1) t = linspace(0,duration,size(audioIn,1)); plot(t,audioIn) subplot(2,1,2) t = linspace(0,duration,size(features,1)); plot(t,features(:,idx.spectralCentroid), ... t,features(:,idx.spectralKurtosis), ... t,features(:,idx.pitch)); legend("Spectral Centroid","Spectral Kurtosis", "Pitch") xlabel("Time (s)")
Create an audio datastore that points to audio samples included with Audio Toolbox™.
folder = fullfile(matlabroot,"toolbox","audio","samples"); ads = audioDatastore(folder);
Create an audioFeatureExtractor object to extract the mel spectrum, Bark spectrum, ERB spectrum, and linear spectrum from each audio file. Use the default analysis window and overlap length for the spectrum extraction.
aFE = audioFeatureExtractor(SampleRate=44.1e3, ... melSpectrum=true, ... barkSpectrum=true, ... erbSpectrum=true, ... linearSpectrum=true);
Call extract to extract the features from each audio file in the datastore. Specify SampleRateMismatchRule as "resample" to resample the audio files in the datastore if they do not match 44.1 kHz, the sample rate of the audioFeatureExtractor object. If you have Parallel Computing Toolbox™, specify UseParallel as true to read the files and extract the features in parallel.
specs = extract(aFE,ads,SampleRateMismatchRule="resample",UseParallel=true);Starting parallel pool (parpool) using the 'Processes' profile ... 17-Dec-2024 09:28:59: Job Queued. Waiting for parallel pool job with ID 3 to start ... Connected to parallel pool with 4 workers.
The specs variable is a numFiles-by-1 cell array, where numFiles is the number of files in the datastore. Each element of the cell array is a numHops-by-numFeatures-by-numChannels array, where the number of hops and number of channels depends on the length and number of channels of the audio file, and the number of features is the requested number of features from the audio data.
numFiles = numel(specs)
numFiles = 39
[numHops1,numFeaturesFile1,numChanelsFile1] = size(specs{1})numHops1 = 1053
numFeaturesFile1 = 620
numChanelsFile1 = 1
[numHops2,numFeaturesFile2,numChanelsFile2] = size(specs{2})numHops2 = 1724
numFeaturesFile2 = 620
numChanelsFile2 = 4
