Music Genre Classification using Machine Learning A Comparative Study

To classify songs into different genres, music researchers have used many different techniques. However, most current approaches rely heavily on feature extraction and subsequent analysis of the extracted features. Deep learning approaches have become increasingly popular, but a comparison between these methods and the five traditional machine
learning algorithms was still needed to give a more accurate representation of how effective they were. Several experiments were run on GTZAN dataset, and obtained promising results with about 66% accuracy.

Keywords

Music Machine Learning Classification CNN

References

zanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on speech and audio processing, 10(5), 293-302
ahuleyan, H. (2018). Music genre classification using machine learning techniques. arXiv preprint arXiv:1804.01149.
Sturm, B. L. (2012, November). An analysis of the GTZAN music genre dataset. In Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies (pp. 7-12)
Sturm, B. L. (2013). The GTZAN dataset: Its contents, its faults,their effects on evaluation, and its future use. arXiv preprintarXiv:1306.1461.
Gemmeke, J. F., Ellis, D. P., Freedman, D., Jansen, A., Lawrence,W., Moore, R. C., ... & Ritter, M. (2017, March). Audio set: Anontology and human-labeled dataset for audio events. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 776-780). IEEE.
Defferrard, M., Benzi, K., Vandergheynst, P., & Bresson, X.(2016). Fma: A dataset for music analysis. arXiv preprintarXiv:1612.01840.
Xu, C., Maddage, N. C., Shao, X., Cao, F., & Tian, Q. (2003,April). Musical genre classification using support vector machines. In 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP’03).(Vol. 5, pp. V-429). IEEE4
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion,B., Grisel, O., ... & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12,2825-2830
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
Choi, K., Fazekas, G., & Sandler, M. (2016). Explaining deep convolutional neural networks on music classification. arXiv preprint arXiv:1607.02444
Ajoodha, R., Klein, R., & Rosman, B. (2015, November). Single-labelled music genre classification using content-based features. In 2015 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-Rob Mech) (pp. 66-71). IEEE.

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Sahil Poonia, Chetan Verma, Nikita Malik . (2022). Music Genre Classification using Machine Learning A Comparative Study. IITM Journal of Management and IT, 13(1), 15-21.

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[1] zanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on speech and audio processing, 10(5), 293-302

[2] ahuleyan, H. (2018). Music genre classification using machine learning techniques. arXiv preprint arXiv:1804.01149.

[3] Sturm, B. L. (2012, November). An analysis of the GTZAN music genre dataset. In Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies (pp. 7-12)

[4] Sturm, B. L. (2013). The GTZAN dataset: Its contents, its faults,their effects on evaluation, and its future use. arXiv preprintarXiv:1306.1461.

[5] Gemmeke, J. F., Ellis, D. P., Freedman, D., Jansen, A., Lawrence,W., Moore, R. C., ... & Ritter, M. (2017, March). Audio set: Anontology and human-labeled dataset for audio events. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 776-780). IEEE.

[6] Defferrard, M., Benzi, K., Vandergheynst, P., & Bresson, X.(2016). Fma: A dataset for music analysis. arXiv preprintarXiv:1612.01840.

[7] Xu, C., Maddage, N. C., Shao, X., Cao, F., & Tian, Q. (2003,April). Musical genre classification using support vector machines. In 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP’03).(Vol. 5, pp. V-429). IEEE4

[8] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion,B., Grisel, O., ... & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12,2825-2830

[9] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.

[10] Choi, K., Fazekas, G., & Sandler, M. (2016). Explaining deep convolutional neural networks on music classification. arXiv preprint arXiv:1607.02444

[11] Ajoodha, R., Klein, R., & Rosman, B. (2015, November). Single-labelled music genre classification using content-based features. In 2015 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-Rob Mech) (pp. 66-71). IEEE.