Abstract
Music Genre Classification is one of the fundamental tasks in the field of Music Information Retrieval (MIR). In this paper the performance of various music genre classification algorithms including Random Forests, Multi-class Support Vector Machines and Deep Belief Networks is being compared. The study is based on the “Million Song Dataset” a freely-available collection of audio features and metadata. The emphasis is put not only on classification accuracy but also on robustness and scalability of algorithms.
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Stokowiec, W. (2016). A Comparative Study on Music Genre Classification Algorithms. In: Ryżko, D., Gawrysiak, P., Kryszkiewicz, M., Rybiński, H. (eds) Machine Intelligence and Big Data in Industry. Studies in Big Data, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-30315-4_11
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DOI: https://doi.org/10.1007/978-3-319-30315-4_11
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