Abstract
Music Information Retrieval (MIR) has been a popular area of research since its inception. Music can be divided into some conventional categories, called music genres, based on a certain set of features such as orchestral instruments, rhythm, and tempo. The MIR community has made tremendous efforts to solve the problem of music genre classification. In this work, we have analysed how the number of genres affects the accuracy of various machine learning models. This paper also presents a novel approach towards the challenge of identifying the genre of a given audio clip using an ensemble model with an accuracy of 90.3%.
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Singhal, K., Chawla, S., Agarwal, A., Goyal, P., Agarwal, A., Rana, P.S. (2023). Ensemble Model for Music Genre Classification. In: Bhattacharya, A., Dutta, S., Dutta, P., Piuri, V. (eds) Innovations in Data Analytics. ICIDA 2022. Advances in Intelligent Systems and Computing, vol 1442. Springer, Singapore. https://doi.org/10.1007/978-981-99-0550-8_32
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DOI: https://doi.org/10.1007/978-981-99-0550-8_32
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