Abstract
Music is a sound that arises sensations in human mind and body. Music, since the beginning of time, has been present in every culture and life form, in audio and symbolic form, and in physical or digital mode of communication. While availability and scope for advancements increase exponentially, so does the need to search, compare, and organise music. Music industry has been striving towards finding the best possible approach to categorise music whether classification on the basis of emotions, instrumentation, genres, or any other music information will be most efficient as well as useful to listeners and music service providers. With the aim to support the best music experience, the current study statistically shows, with the help of prior research in music information retrieval and implementation of several powerful machine learning-based technologies, that genre classification, that too, ensemble-based, can be as accurate as 73.17%. The study analyses the performances of all models towards genre classification and concludes by proving max-voting ensemble-based models to be more accurate than each component classifier and advanced ensemble models and also optimal for real-world music genre classification as compared to prior experiments on GTZAN database, which is the novel contribution of the study.
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Acknowledgements
The authors are grateful to George Tzanetakis for availability of his dataset for our experimental work which was encouraged by Mr. Rahul Gupta, and the authors thank them for their perpetual guidance and vision.
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Gupta, R., Yadav, J., Kapoor, C. (2021). Music Information Retrieval and Intelligent Genre Classification. In: Pandian, A.P., Palanisamy, R., Ntalianis, K. (eds) Proceedings of International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1272. Springer, Singapore. https://doi.org/10.1007/978-981-15-8443-5_17
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