openl3Preprocess

Download and unzip the Audio Toolbox™ model for OpenL3.

Type openl3 at the Command Window. If the Audio Toolbox model for OpenL3 is not installed, the function provides a link to the location of the network weights. To download the model, click the link. Unzip the file to a location on the MATLAB path.

Alternatively, execute these commands to download and unzip the OpenL3 model to your temporary directory.

downloadFolder = fullfile(tempdir,'OpenL3Download');
loc = websave(downloadFolder,'https://ssd.mathworks.com/supportfiles/audio/openl3.zip');
OpenL3Location = tempdir;
unzip(loc,OpenL3Location)
addpath(fullfile(OpenL3Location,'openl3'))

Check that the installation is successful by typing openl3 at the Command Window. If the network is installed, then the function returns a DAGNetwork (Deep Learning Toolbox) object.

openl3

ans = 
  DAGNetwork with properties:

         Layers: [30×1 nnet.cnn.layer.Layer]
    Connections: [29×2 table]
     InputNames: {'in'}
    OutputNames: {'out'}

Use openl3Preprocess to extract embeddings from an audio signal.

Read in an audio signal.

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

To extract spectrograms from the audio, call the openl3Preprocess function with the audio and sample rate. Use 50% overlap and set the spectrum type to linear. The openl3Preprocess function returns an array of 30 spectrograms produced using an FFT length of 512.

features = openl3Preprocess(audioIn,fs,'OverlapPercentage',50,'SpectrumType','linear');
[posFFTbinsOvLap50,numHopsOvLap50,~,numSpectOvLap50] = size(features)

posFFTbinsOvLap50 = 257

numHopsOvLap50 = 197

numSpectOvLap50 = 30

Call openl3Preprocess again, this time using the default overlap of 90%. The openl3Preprocess function now returns an array of 146 spectrograms.

features = openl3Preprocess(audioIn,fs,'SpectrumType','linear');
[posFFTbinsOvLap90,numHopsOvLap90,~,numSpectOvLap90] = size(features)

posFFTbinsOvLap90 = 257

numHopsOvLap90 = 197

numSpectOvLap90 = 146

Visualize one of the spectrograms at random.

randSpect = randi(numSpectOvLap90);
viewRandSpect = features(:,:,:,randSpect);
N = size(viewRandSpect,2); 
binsToHz = (0:N-1)*fs/N;
nyquistBin = round(N/2);
semilogx(binsToHz(1:nyquistBin),mag2db(abs(viewRandSpect(1:nyquistBin))))
xlabel('Frequency (Hz)')
ylabel('Power (dB)');
title([num2str(randSpect),'th Spectrogram'])
axis tight
grid on

Create an OpenL3 network (this requires Deep Learning Toolbox) using the same 'SpectrumType'.

net = openl3('SpectrumType','linear');

Extract and visualize the audio embeddings.

embeddings = predict(net,features);
surf(embeddings,'EdgeColor','none')
view([90,-90])
axis([1 numSpectOvLap90 1 numSpectOvLap90])
xlabel('Embedding Length')
ylabel('Spectrum Number')
title('OpenL3 Feature Embeddings')
axis tight