Efficient Robust Music Genre Classification with Depthwise Separable Convolutions and Source Separation

Abstract Given recent advances in deep music source separation, a feature representation method is proposed that combines source separation with a state-of-the-art representation learning technique that is suitably repurposed for computer audition (i.e. machine listening). A depthwise separable convolutional neural network is trained on a challenging electronic dance music (EDM) data set and its performance is compared to convolutional neural networks operating on both source separated and standard spectrograms. It is shown that source separation improves classification performance in a limited-data setting compared to the standard single spectrogram approach.