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Train Spoken Digit Recognition Network Using Out-of-Memory Features

This example trains a spoken digit recognition network on out-of-memory auditory spectrograms using a transformed datastore. In this example, you extract auditory spectrograms from audio using audioDatastore (Audio Toolbox) and audioFeatureExtractor (Audio Toolbox), and you write them to disk. You then use a signalDatastore to access the features during training. The workflow is useful when the training features do not fit in memory. In this workflow, you only extract features once, which speeds up your workflow if you are iterating on the deep learning model design.


Download the Free Spoken Digit Data Set (FSDD). FSDD consists of 2000 recordings of four speakers saying the numbers 0 through 9 in English.

downloadFolder = matlab.internal.examples.downloadSupportFile("audio","");
dataFolder = tempdir;
dataset = fullfile(dataFolder,"FSDD");

Create an audioDatastore that points to the dataset.

ads = audioDatastore(dataset,IncludeSubfolders=true);

Display the classes and the number of examples in each class.

[~,filenames] = fileparts(ads.Files);
ads.Labels = categorical(extractBefore(filenames,'_'));
     0      200 
     1      200 
     2      200 
     3      200 
     4      200 
     5      200 
     6      200 
     7      200 
     8      200 
     9      200 

Split the FSDD into training and test sets. Allocate 80% of the data to the training set and retain 20% for the test set. You use the training set to train the model and the test set to validate the trained model.

rng default
ads = shuffle(ads);
[adsTrain,adsTest] = splitEachLabel(ads,0.8);
ans=10×2 table
    Label    Count
    _____    _____

      0       160 
      1       160 
      2       160 
      3       160 
      4       160 
      5       160 
      6       160 
      7       160 
      8       160 
      9       160 

ans=10×2 table
    Label    Count
    _____    _____

      0       40  
      1       40  
      2       40  
      3       40  
      4       40  
      5       40  
      6       40  
      7       40  
      8       40  
      9       40  

Reduce Training Dataset

To train the network with the entire dataset and achieve the highest possible accuracy, set speedupExample to false. To run this example quickly, set speedupExample to true.

speedupExample = false;
if speedupExample
    adsTrain = splitEachLabel(adsTrain,2);
    adsTest = splitEachLabel(adsTest,2);

Set up Auditory Spectrogram Extraction

The CNN accepts mel-frequency spectrograms.

Define parameters used to extract mel-frequency spectrograms. Use 220 ms windows with 10 ms hops between windows. Use a 2048-point DFT and 40 frequency bands.

fs = 8000;

frameDuration = 0.22;
frameLength = round(frameDuration*fs);

hopDuration = 0.01;
hopLength = round(hopDuration*fs);

segmentLength = 8192;

numBands = 40;
fftLength = 2048;

Create an audioFeatureExtractor (Audio Toolbox) object to compute mel-frequency spectrograms from input audio signals.

afe = audioFeatureExtractor(melSpectrum=true,SampleRate=fs, ...
    Window=hamming(frameLength,"periodic"),OverlapLength=frameLength - hopLength, ...

Set the parameters for the mel-frequency spectrogram.

setExtractorParameters(afe,"melSpectrum",NumBands=numBands,FrequencyRange=[50 fs/2],WindowNormalization=true);

Create a transformed datastore that computes mel-frequency spectrograms from audio data. The supporting function, getSpeechSpectrogram, standardizes the recording length and normalizes the amplitude of the audio input. getSpeechSpectrogram uses the audioFeatureExtractor object afe to obtain the log-based mel-frequency spectrograms.

adsSpecTrain = transform(adsTrain,@(x)getSpeechSpectrogram(x,afe,segmentLength));

Write Auditory Spectrograms to Disk

Use writeall (Audio Toolbox) to write auditory spectrograms to disk. Set UseParallel to true to perform writing in parallel.

outputLocation = fullfile(tempdir,"FSDD_Features");
Starting parallel pool (parpool) using the 'Processes' profile ...
Connected to parallel pool with 8 workers.

Set up Training Signal Datastore

Create a signalDatastore that points to the out-of-memory features. The read function returns a spectrogram/label pair.

sds = signalDatastore(outputLocation,IncludeSubfolders=true, ...

Validation Data

The validation dataset fits into memory. Precompute validation features.

adsTestT = transform(adsTest,@(x){getSpeechSpectrogram(x,afe,segmentLength)});
XTest = readall(adsTestT);
XTest = cat(4,XTest{:});

Get the validation labels.

YTest = adsTest.Labels;

Define CNN Architecture

Construct a small CNN as an array of layers. Use convolutional and batch normalization layers, and downsample the feature maps using max pooling layers. To reduce the possibility of the network memorizing specific features of the training data, add a small amount of dropout to the input to the last fully connected layer.

sz = size(XTest);
specSize = sz(1:2);
imageSize = [specSize 1];

numClasses = numel(categories(YTest));

dropoutProb = 0.2;
numF = 12;
layers = [









classes = categories(YTest);

Set the hyperparameters to use in training the network. Use a mini-batch size of 50 and a learning rate of 1e-4. Specify "adam" optimization. To use the parallel pool to read the transformed datastore, set PreprocessingEnvironment to "parallel". For more information, see trainingOptions (Deep Learning Toolbox).

miniBatchSize = 50;
options = trainingOptions("adam", ...
    InitialLearnRate=1e-4, ...
    MaxEpochs=30, ...
    LearnRateSchedule="piecewise", ...
    LearnRateDropFactor=0.1, ...
    LearnRateDropPeriod=15, ...
    MiniBatchSize=miniBatchSize, ...
    Shuffle="every-epoch", ...
    Plots="training-progress", ...
    Metrics="accuracy", ...
    Verbose=false, ...
    ValidationData={XTest,YTest}, ...
    ValidationFrequency=ceil(numel(adsTrain.Files)/miniBatchSize), ...
    ExecutionEnvironment="auto", ...

Train the network by passing the training datastore to trainnet.

trainedNet = trainnet(sds,layers,"crossentropy",options);

Use the trained network to predict the digit labels for the test set.

scores = predict(trainedNet,XTest);
Ypredicted = scores2label(scores,classes);
cnnAccuracy = sum(Ypredicted==YTest)/numel(YTest)*100
cnnAccuracy = 96

Summarize the performance of the trained network on the test set with a confusion chart. Display the precision and recall for each class by using column and row summaries. The table at the bottom of the confusion chart shows the precision values. The table to the right of the confusion chart shows the recall values.

figure(Units="normalized",Position=[0.2 0.2 1.5 1.5]);
confusionchart(YTest,Ypredicted, ...
    Title="Confusion Chart for DCNN", ...

Supporting Functions

Get Speech Spectrograms

function X = getSpeechSpectrogram(x,afe,segmentLength)
% getSpeechSpectrogram(x,afe,params) computes a speech spectrogram for the
% signal x using the audioFeatureExtractor afe.

x = scaleAndResize(single(x),segmentLength);

spec = extract(afe,x).';

X = log10(spec + 1e-6);


Scale and Resize

function x = scaleAndResize(x,segmentLength)
% scaleAndResize(x,segmentLength) scales x by its max absolute value and forces
% its length to be segmentLength by trimming or zero-padding.

L = segmentLength;
N = size(x,1);
if N > L
    x = x(1:L,:);
elseif N < L
    pad = L - N;
    prepad = floor(pad/2);
    postpad = ceil(pad/2);
    x = [zeros(prepad,1);x;zeros(postpad,1)];
x = x./max(abs(x));


Custom Write Function

function myCustomWriter(spec,writeInfo,~)
% myCustomWriter(spec,writeInfo,~) writes an auditory spectrogram/label
% pair to MAT files.

filename = strrep(writeInfo.SuggestedOutputName,".wav",".mat");
label = writeInfo.ReadInfo.Label;


See Also

| (Deep Learning Toolbox)