Hi Hamza,
I understand that you want to implement speech emotion recognition using CNN. Here are a few suggestions to improve the performance of the model:
As a part of audio pre-processing, you can implement feature extraction and audio augmentation techniques before passing the data to the CNN model.
You can first extract features like ZCR (Zero Crossing Rate), MFCC (Mel-Frequency Cepstral Coefficients) and Mel Spectrum using the "audioFeatureExtractor" function. You can refer to the following documentation link for more information on the function:
Followed by feature extraction, you can augment the audio by adding noise, stretching and shifting the pitch using the "audioDataAugmenter" function. You can refer to the following documentation link for more information on the function:
You can also encode the data labels into one-hot vectors using the "onehotencode" function:
In the network architecture, you can include batch normalization layers, dropout layers and max pooling layers as well for faster convergence and regularization. You can refer to the following code snippet for reference:
Also, your maximum number of epochs is set to 500, which might be too high and could lead to overfitting. You might want to consider using early stopping to prevent this. You can introduce "ValidationData", "ValidationFrequency" and "ValidationPatience" training options for validation stopping. You can refer to the following documentation link for more information on specifying training options:
Hope this helps!
