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Detect Air Compressor Sounds in Simulink Using YAMNet

This example shows how to use a pretrained network obtained from transfer learning within a Simulink® model to classify audio signals obtained from an air compressor.

The network is pretrained using a data set that contains recordings from air compressors. The data set is classified into one healthy state and seven faulty states, for a total of eight classes. For more information on training, see Transfer Learning Using YAMNet.

To download this pretrained network and a set of air compressor sounds to detect, run the following commands. These commands download and unzip the files to a location on the MATLAB® path. The airCompressorNet.mat file stores the pretrained network.

url = '';
AirCompressorLocation = tempdir;
dataFolder = fullfile(AirCompressorLocation,'YAMNetTransferLearning');

if ~exist(dataFolder,'dir')
    disp('Downloading pretrained network ...')

Open the detectsound.slx model. Click the Select Compressor State block. The default type of sound is set to 'Bearing'. The model contains a YAMNet Preprocess block followed by an Image Classifier (Deep Learning Toolbox) block.

Run the model. The YAMNet Preprocess block generates 96-by-64 sized mel spectrograms from the input audio. The Image Classifier block uses the airCompressorNet.mat file and classifies the signal into one of the eight classes the model is trained on. The label of the predicted class is displayed using the Display block. The scope shows the score of the predicted class and the other classes.


While the simulation is running, you can change the input sound by double clicking the Select Compressor State block and choosing a type of sound from the drop-down menu.

Select 'Healthy' while the simulation is running. The Display block updates the predicted label and the Scope block shows the new scores.

See Also

(Deep Learning Toolbox) |

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