Machine Learning Based Music Categorization

Author(s)	Ms. Princy tyagi, Narendra Singh, Satyam Singh Rawat, Anirudh Ratauri, Rohit Singh Rawat
Country	India
Abstract	Music genre categorization is an essential activity in music data retrieval and recommendation systems. This research focuses on classifying music genres using machine learning techniques, specifically the Support Vector Machine. The GTZAN dataset, comprising 10 distinct genres, is utilized for training and evaluation. We took audio features like MFCCs, spectral contrast, and chroma vectors from the GTZAN dataset and used them to train a Support Vector Machine (SVM) model. The categorization model achieved an accuracy of 81.1% across 10 distinct genres in a multi-class setting the research emphasizes the difficulties of genre convergence and the efficacy of machine learning in automating music categorization. Future developments might explore deep learning techniques, like Convolutional Neural Networks, better ways to choose features, and improving data to make music categorization more effective. assignment in music retrieve information
Keywords	Music Genre Categorization, Machine Learning, Support Vector Machine, GTZAN Dataset, Feature Extraction.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-19
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.48623
Short DOI	https://doi.org/g9qw9n

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