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Transfer Learning with Pretrained Audio Networks in Deep Network Designer

This example shows how to interactively fine-tune a pretrained network to classify new audio signals using Deep Network Designer.

Transfer learning is commonly used in deep learning applications. You can take a pretrained network and use it as a starting point to learn a new task. Fine-tuning a network with transfer learning is usually much faster and easier than training a network with randomly initialized weights from scratch. You can quickly transfer learned features to a new task using a smaller number of training signals.

This example retrains YAMNet, a pretrained convolutional neural network, to classify a new set of audio signals.

Load Data

Download and unzip the air compressor data set [1]. This data set consists of recordings from air compressors in a healthy state or one of seven faulty states.

zipFile = matlab.internal.examples.downloadSupportFile('audio','AirCompressorDataset/');
dataFolder = fileparts(zipFile);

Create an audioDatastore object to manage the data.

ads = audioDatastore(dataFolder,IncludeSubfolders=true,LabelSource="foldernames");

Split the data into training, validation, and test sets using the splitEachLabel function.

[adsTrain,adsValidation,adsTest] = splitEachLabel(ads,0.7,0.2,0.1);

Use the transform function to preprocess the data using the function audioPreprocess, found at the end of this example. For each signal:

  • Use yamnetPreprocess (Audio Toolbox) to generate mel spectrograms suitable for training using YAMNet. Each audio signal produces multiple spectrograms.

  • Duplicate the class label for each of the spectrograms.

tdsTrain = transform(adsTrain,@audioPreprocess,IncludeInfo=true);
tdsValidation = transform(adsValidation,@audioPreprocess,IncludeInfo=true);
tdsTest = transform(adsTest,@audioPreprocess,IncludeInfo=true);

Select Pretrained Network

Prepare and train the network interactively using Deep Network Designer. To open Deep Network Designer, on the Apps tab, under Machine Learning and Deep Learning, click the app icon. Alternatively, you can open the app from the command line.


Deep Network Designer provides a selection of pretrained audio classification networks. These models require both Audio Toolbox™ and Deep Learning Toolbox™.

Under Audio Networks, select YAMNet from the list of pretrained networks and click Open. If the Audio Toolbox model for YAMNet is not installed, click Install instead. Deep Network Designer provides a link to the location of the network weights. Unzip the file to a location on the MATLAB path. Now close the Deep Network Designer Start Page and reopen it. When the network is correctly installed and on the path, you can click the Open button on YAMNet. The YAMNet model can classify audio into one of 521 sound categories. For more information, see yamnet (Audio Toolbox).

Deep Network Designer displays a zoomed-out view of the whole network in the Designer pane. To zoom in with the mouse, use Ctrl+scroll wheel. To pan, use the arrow keys, or hold down the scroll wheel and drag the mouse. Select a layer to view its properties. Clear all layers to view the network summary in the Properties pane.

Prepare Network for Transfer Learning

To prepare the network for transfer learning, in the Designer pane, replace the last learnable layer and the final classification layer.

Replace Last Learnable Layer

To use a pretrained network for transfer learning, you must change the number of classes to match your new data set. First, find the last learnable layer in the network. For YAMNet, the last learnable layer is the last fully connected layer, dense.

Drag a new fullyConnectedLayer onto the canvas. The OutputSize property defines the number of classes for classification problems. Change OutputSize to the number of classes in the new data, in this example, 8.

Change the learning rates so that learning is faster in the new layer than in the transferred layers by setting WeightLearnRateFactor and BiasLearnRateFactor to 10.

Delete the last fully connected layer and connect your new layer instead.

Replace Output Layer

For transfer learning, you need to replace the output layer. Scroll to the end of the Layer Library and drag a new classificationLayer onto the canvas. Delete the original classification layer and connect your new layer in its place.

For a new output layer, you do not need to set OutputSize. At training time, Deep Network Designer automatically sets the output classes of the layer from the data.

Check Network

To check that the network is ready for training, click Analyze. If the Deep Learning Network Analyzer reports zero errors, then the edited network is ready for training.

Import Data

To load the data into Deep Network Designer, on the Data tab, click Import Data > Import Datastore. Select tdsTrain as the training data and tdsValidation as the validation data.

Using Deep Network Designer, you can inspect the training and validation data in the Data tab. You can see that the data is as expected prior to training.

Select Training Options

To specify the training options, select the Training tab and click Training Options. Set the initial learning rate to a small value to slow down learning in the transferred layers. In combination with the increased learning rate factors for the fully connected layer, learning is now fast only in the new layers and slower in the other layers.

For this example, set Solver to adam, InitialLearnRate to 0.0001, and MaxEpochs to 2.

Train Network

To train the network with the specified training options, click Close and then click Train.

Deep Network Designer allows you to visualize and monitor the training progress. You can then edit the training options and retrain the network, if required. To find the optimal training options, create a deep learning experiment using Experiment Manager. You can create a deep learning experiment in Deep Network Designer by clicking Export > Create Experiment.

To export the results from training, on the Training tab, select Export > Export Trained Network and Results. Deep Network Designer exports the trained network as the variable trainedNetwork_1 and the training info as the variable trainInfoStruct_1.

You can also generate MATLAB code, which recreates the network and the training options used. On the Training tab, select Export > Generate Code for Training. Examine the MATLAB code to learn how to programmatically prepare the data for training, create the network architecture, and train the network.

Test Network

Classify the test data using the exported network and the classify function.

data = readall(tdsTest);
YTest = [data{:,2}];
YPred = classify(trainedNetwork_1,tdsTest);

accuracy = sum(YPred == YTest')/numel(YTest)
accuracy = 0.9830

Supporting Function

The function audioPreprocess uses yamnetPreprocess (Audio Toolbox) to generate mel spectrograms from audioIn that you can feed to the YAMNet pretrained network. Each input signal generates multiple spectrograms, so the labels must be duplicated to create a one-to-one correspondence with the spectrograms.

function [data,info] = audioPreprocess(audioIn,info)
class = info.Label;
fs = info.SampleRate;
features = yamnetPreprocess(audioIn,fs);

numSpectrograms = size(features,4);

data = cell(numSpectrograms,2);
for index = 1:numSpectrograms
    data{index,1} = features(:,:,:,index);
    data{index,2} = class;


[1] Verma, Nishchal K., Rahul Kumar Sevakula, Sonal Dixit, and Al Salour. “Intelligent Condition Based Monitoring Using Acoustic Signals for Air Compressors.” IEEE Transactions on Reliability 65, no. 1 (March 2016): 291–309.

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