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Open Access Publications from the University of California

Predicting Musical Genres using Deep Learning and Ensembling

  • Author(s): Sang, Andrew Minkyu
  • Advisor(s): Wu, Yingnian
  • et al.
Abstract

Automatic Music Genre Classification is a core problem in the Music Information Retrieval space. The classification approach detailed in this paper involves: using musical features from the Million Song Dataset, augmenting the musical dataset with lyrics and cover art images, building a deep learning model for each of the three different types of inputs, and then ensembling the predictions from the individual models using a gradient boosted machine. Ensembling resulted in an 8.6% increase in F1 score over the best individual model while maintaining a similar level of accuracy. This framework may be successfully applied to other problems with multimodal inputs.

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