vggishPreprocess
Syntax
Description
Examples
Read in an audio signal to extract feature embeddings from it.
[audioIn,fs] = audioread(
"Ambiance-16-44p1-mono-12secs.wav");Plot and listen to the audio signal.
t = (0:numel(audioIn)-1)/fs; plot(t,audioIn) xlabel("Time (s)") ylabel("Ampltiude") axis tight
sound(audioIn,fs)
VGGish requires you to preprocess the audio signal to match the input format used to train the network. The preprocesssing steps include resampling the audio signal and computing an array of mel spectrograms. To learn more about mel spectrograms, see melSpectrogram. Use vggishPreprocess to preprocess the signal and extract the mel spectrograms to be passed to VGGish. Visualize one of these spectrograms chosen at random.
spectrograms = vggishPreprocess(audioIn,fs); arbitrarySpect = spectrograms(:,:,1,randi(size(spectrograms,4))); surf(arbitrarySpect,EdgeColor="none") view(90,-90) xlabel("Mel Band") ylabel("Frame") title("Mel Spectrogram for VGGish") axis tight
Create a VGGish neural network using the audioPretrainedNetwork function.
net = audioPretrainedNetwork("vggish");Call predict with the network on the preprocessed mel spectrogram images to extract feature embeddings. The feature embeddings are returned as a numFrames-by-128 matrix, where numFrames is the number of individual spectrograms and 128 is the number of elements in each feature vector.
features = predict(net,spectrograms); [numFrames,numFeatures] = size(features)
numFrames = 24
numFeatures = 128
Visualize the VGGish feature embeddings.
surf(features,EdgeColor="none") view([90 -90]) xlabel("Feature") ylabel("Frame") title("VGGish Feature Embeddings") axis tight
In this example, you transfer the learning in the VGGish regression model to an audio classification task.
Download and unzip the environmental sound classification data set. This data set consists of recordings labeled as one of 10 different audio sound classes (ESC-10).
downloadFolder = matlab.internal.examples.downloadSupportFile("audio","ESC-10.zip"); unzip(downloadFolder,tempdir) dataLocation = fullfile(tempdir,"ESC-10");
Create an audioDatastore object to manage the data and split it into train and validation sets. Call countEachLabel to display the distribution of sound classes and the number of unique labels.
ads = audioDatastore(dataLocation,IncludeSubfolders=true,LabelSource="foldernames");
labelTable = countEachLabel(ads)labelTable=10×2 table
Label Count
______________ _____
chainsaw 40
clock_tick 40
crackling_fire 40
crying_baby 40
dog 40
helicopter 40
rain 40
rooster 38
sea_waves 40
sneezing 40
Determine the total number of classes and their names.
numClasses = height(labelTable); classNames = unique(ads.Labels);
Call splitEachLabel to split the data set into train and validation sets. Inspect the distribution of labels in the training and validation sets.
[adsTrain, adsValidation] = splitEachLabel(ads,0.8); countEachLabel(adsTrain)
ans=10×2 table
Label Count
______________ _____
chainsaw 32
clock_tick 32
crackling_fire 32
crying_baby 32
dog 32
helicopter 32
rain 32
rooster 30
sea_waves 32
sneezing 32
countEachLabel(adsValidation)
ans=10×2 table
Label Count
______________ _____
chainsaw 8
clock_tick 8
crackling_fire 8
crying_baby 8
dog 8
helicopter 8
rain 8
rooster 8
sea_waves 8
sneezing 8
The VGGish network expects audio to be preprocessed into log mel spectrograms. Use vggishPreprocess to extract the spectrograms from the train set. There are multiple spectrograms for each audio signal. Replicate the labels so that they are in one-to-one correspondence with the spectrograms.
overlapPercentage =75; trainFeatures = []; trainLabels = []; while hasdata(adsTrain) [audioIn,fileInfo] = read(adsTrain); features = vggishPreprocess(audioIn,fileInfo.SampleRate,OverlapPercentage=overlapPercentage); numSpectrograms = size(features,4); trainFeatures = cat(4,trainFeatures,features); trainLabels = cat(2,trainLabels,repelem(fileInfo.Label,numSpectrograms)); end
Extract spectrograms from the validation set and replicate the labels.
validationFeatures = []; validationLabels = []; segmentsPerFile = zeros(numel(adsValidation.Files), 1); idx = 1; while hasdata(adsValidation) [audioIn,fileInfo] = read(adsValidation); features = vggishPreprocess(audioIn,fileInfo.SampleRate,OverlapPercentage=overlapPercentage); numSpectrograms = size(features,4); validationFeatures = cat(4,validationFeatures,features); validationLabels = cat(2,validationLabels,repelem(fileInfo.Label,numSpectrograms)); segmentsPerFile(idx) = numSpectrograms; idx = idx + 1; end
Load the VGGish model and using audioPretrainedNetwork.
net = audioPretrainedNetwork("vggish");Use addLayers (Deep Learning Toolbox) to add a fullyConnectedLayer (Deep Learning Toolbox) and a softmaxLayer (Deep Learning Toolbox) to the network. Set the WeightLearnRateFactor and BiasLearnRateFactor of the new fully connected layer to 10 so that learning is faster in the new layer than in the transferred layers.
net = addLayers(net,[ ... fullyConnectedLayer(numClasses,Name="FCFinal",WeightLearnRateFactor=10,BiasLearnRateFactor=10) softmaxLayer(Name="softmax")]);
Use connectLayers (Deep Learning Toolbox) to append the fully connected and softmax layers to the network.
net = connectLayers(net,"EmbeddingBatch","FCFinal");
To define training options, use trainingOptions (Deep Learning Toolbox).
miniBatchSize = 128; options = trainingOptions("adam", ... MaxEpochs=5, ... MiniBatchSize=miniBatchSize, ... Shuffle="every-epoch", ... ValidationData={validationFeatures,validationLabels'}, ... ValidationFrequency=50, ... LearnRateSchedule="piecewise", ... LearnRateDropFactor=0.5, ... LearnRateDropPeriod=2, ... OutputNetwork="best-validation-loss", ... Verbose=false, ... Plots="training-progress",... Metrics="accuracy");
To train the network, use trainnet.
[trainedNet,netInfo] = trainnet(trainFeatures,trainLabels',net,"crossentropy",options);
Each audio file was split into several segments to feed into the VGGish network. Combine the predictions for each file in the validation set using a majority-rule decision.
scores = predict(trainedNet,validationFeatures); validationPredictions = scores2label(scores,classNames); idx = 1; validationPredictionsPerFile = categorical; for ii = 1:numel(adsValidation.Files) validationPredictionsPerFile(ii,1) = mode(validationPredictions(idx:idx+segmentsPerFile(ii)-1)); idx = idx + segmentsPerFile(ii); end
Use confusionchart (Deep Learning Toolbox) to evaluate the performance of the network on the validation set.
figure(Units="normalized",Position=[0.2 0.2 0.5 0.5]); confusionchart(adsValidation.Labels,validationPredictionsPerFile, ... Title=sprintf("Confusion Matrix for Validation Data \nAccuracy = %0.2f %%",mean(validationPredictionsPerFile==adsValidation.Labels)*100), ... ColumnSummary="column-normalized", ... RowSummary="row-normalized")
Read in an audio signal
[audioIn,fs] = audioread("SpeechDFT-16-8-mono-5secs.wav");Use audioViewer to visualize and listen to the audio.
audioViewer(audioIn,fs)
Use vggishPreprocess to generate mel spectrograms that can be fed to the VGGish pretrained network. Specify additional outputs to get the center frequencies of the bands and the locations of the windows in time.
[spectrograms,cf,ts] = vggishPreprocess(audioIn,fs);
Choose a random spectrogram from the input to visualize. Use the center frequency and time location information to label the axes.
spectIdx = randi(size(spectrograms,4)); randSpect = spectrograms(:,:,1,spectIdx); surf(cf,ts(:,spectIdx),randSpect,EdgeColor="none") view([90 -90]) xlabel("Frequency (Hz)") ylabel("Time (s)") axis tight
Input Arguments
Output Arguments
References
[1] Gemmeke, Jort F., et al. “Audio Set: An Ontology and Human-Labeled Dataset for Audio Events.” 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2017, pp. 776–80. DOI.org (Crossref),doi:10.1109/ICASSP.2017.7952261.
[2] Hershey, Shawn, et al. “CNN Architectures for Large-Scale Audio Classification.” 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2017, pp. 131–35. DOI.org (Crossref), doi:10.1109/ICASSP.2017.7952132.
