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Machine Learning Evaluation for Music Genre Classification of Audio Signals

Machine Learning Evaluation for Music Genre Classification of Audio Signals

Chetna Dabas, Aditya Agarwal, Naman Gupta, Vaibhav Jain, Siddhant Pathak
Copyright: © 2020 |Volume: 12 |Issue: 3 |Pages: 11
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781799805632|DOI: 10.4018/IJGHPC.2020070104
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MLA

Dabas, Chetna, et al. "Machine Learning Evaluation for Music Genre Classification of Audio Signals." IJGHPC vol.12, no.3 2020: pp.57-67. http://doi.org/10.4018/IJGHPC.2020070104

APA

Dabas, C., Agarwal, A., Gupta, N., Jain, V., & Pathak, S. (2020). Machine Learning Evaluation for Music Genre Classification of Audio Signals. International Journal of Grid and High Performance Computing (IJGHPC), 12(3), 57-67. http://doi.org/10.4018/IJGHPC.2020070104

Chicago

Dabas, Chetna, et al. "Machine Learning Evaluation for Music Genre Classification of Audio Signals," International Journal of Grid and High Performance Computing (IJGHPC) 12, no.3: 57-67. http://doi.org/10.4018/IJGHPC.2020070104

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Abstract

Music genre classification has its own popularity index in the present times. Machine learning can play an important role in the music streaming task. This research article proposes a machine learning based model for the classification of music genre. The evaluation of the proposed model is carried out while considering different music genres as in blues, metal, pop, country, classical, disco, jazz and hip-hop. Different audio features utilized in this study include MFCC (Mel Frequency Spectral Coefficients), Delta, Delta-Delta and temporal aspects for processing the data. The implementation of the proposed model has been done in the Python language. The results of the proposed model reveal an accuracy SVM accuracy of 95%. The proposed algorithm has been compared with existing algorithms and the proposed algorithm performs better than the existing ones in terms of accuracy.

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