Bit-Chan Eom, Dong-Hwi Cho, Choon-Sung Nam, Journal of Internet Computing and Services, Vol. 25, No. 4, pp. 57-64, Aug. 2024
10.7472/jksii.2024.25.4.57, Full Text:
Keywords: Music genre classification, Machine Learning, Music feature extraction, GTZAN, Support Vector Machine

Abstract

This paper aims to enhance the accuracy of music genre classification for music tracks where genre information is not provided, by utilizing machine learning to classify a large amount of music data. The paper proposes collecting and preprocessing data instead of using the commonly employed GTZAN dataset in previous research for genre classification in music. To create a dataset with superior classification performance compared to the GTZAN dataset, we extract specific segments with the highest energy level of the onset. We utilize 57 features as the main characteristics of the music data used for training, including Mel Frequency Cepstral Coefficients (MFCC). We achieved a training accuracy of 85% and a testing accuracy of 71% using the Support Vector Machine (SVM) model to classify into Classical, Jazz, Country, Disco, Soul, Rock, Metal, and Hiphop genres based on preprocessed data.

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