Kim J, Won M, Serra X, Liem C.C.S. Transfer Learning of Artist Group Factors to Musical Genre Classification. Proceedings of the 2018 World Wide Web Conference

The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy. Artist labels are less subjective and less noisy, while certain artists may relate more strongly to certain genres. At the same time, at prediction time, it is not guaranteed that artist labels are available for a given audio segment. Therefore, in this work, we propose to apply the transfer learning framework, learning artist-related information which will be used at inference time for genre classification. We consider different types of artist-related information, expressed through artist group factors, which will allow for more efficient learning and stronger robustness to potential label noise. Furthermore, we investigate how to achieve the highest validation accuracy on the given FMA dataset, by experimenting with various kinds of transfer methods, including single-task transfer, multi-task transfer and finally multi-task learning.

Additional material:

• The experiments were run on GPU-accelerated hardware and software environments. We used Lasagne (see in Zenodo), Theano (see in arXiv) and Keras (see in Github) as main experimental frameworks. The main Software for the experiments can be found in GitHub
• FMA dataset
• Post-print at UPF e-repository