  Given recent advances in deep music source separation, we propose a feature
representation method that combines source separation with a state-of-the-art
representation learning technique that is suitably repurposed for computer
audition (i.e. machine listening). We train a depthwise separable convolutional
neural network on a challenging electronic dance music (EDM) data set and
compare its performance 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.
