Abstract

We present USC SAIL’s submission to the 2020 Emotions and Themes in Music challenge: an ensemble-based convolutional neural network (CNN) model trained using various loss functions. In this work, we investigate the effect of different loss functions and resampling strategies on prediction performance, finding that using focal loss improves overall performance on the provided imbalanced, multi-label dataset. Additionally, we report results from varying the receptive field on our base classifier—a CNN-based architecture trained using Mel spectrograms—which also results in better model performance. We conclude that the choice of the loss function is paramount for improving on existing methods in music tagging, particularly in the presence of class imbalance.

Date

January 1, 1970

Authors

Dillon Knox, Timothy Greer, Benjamin Ma, Emily Kuo, Krishna Somandepalli, Shrikanth Narayanan

Conference

MediaEval