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Home / Archives / Volume-4 / Issue-3 / Article-3

Volume - 4 | Issue - 3 | september 2022

Classification of Music Genres using Feature Selection and Hyperparameter Tuning
Rahul Singhal  , Shruti Srivatsan, Priyabrata Panda  184  100
Pages: 167-178
Cite this article
Singhal, R., Srivatsan, S. & Panda, P. (2022). Classification of Music Genres using Feature Selection and Hyperparameter Tuning. Journal of Artificial Intelligence and Capsule Networks, 4(3), 167-178. doi:10.36548/jaicn.2022.3.003
Published
25 August, 2022
Abstract

The ability of music to spread joy and excitement across lives, makes it widely acknowledged as the human race's universal language. The phrase "music genre" is frequently used to group several musical styles together as following a shared custom or set of guidelines. According to their unique preferences, people now make playlists based on particular musical genres. Due to the determination and extraction of appropriate audio elements, music genre identification is regarded as a challenging task. Music information retrieval, which extracts meaningful information from music, is one of several real - world applications of machine learning. The objective of this paper is to efficiently categorise songs into various genres based on their attributes using various machine learning approaches. To enhance the outcomes, appropriate feature engineering and data pre-processing techniques have been performed. Finally, using suitable performance assessment measures, the output from each model has been compared. Compared to other machine learning algorithms, Random Forest along with efficient feature selection and hyperparameter tuning has produced better results in classifying music genres.

Keywords

Feature selection hyperparameter tuning music genre classification Music Information Retrieval (MIR)

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