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

Authors

  • Gabriel Mersy University of Minnesota

Keywords:

Machine Perception, Applications Of AI, Deep Learning, Machine Learning

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.

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Published

2021-05-18

How to Cite

Mersy, G. (2021). Efficient Robust Music Genre Classification with Depthwise Separable Convolutions and Source Separation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15972-15973. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17982

Issue

Section

AAAI Undergraduate Consortium