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Music Genre Detection using Deep Learning Models

Ayush Kishore Mishra*, Divyansh Kumar Singh**, Ankit Khare***

*-*** Department of Information Technology, Shri Ramswaroop Memorial College of Engineering and Management Lucknow, Uttar Pradesh, India.

Periodicity:March - May'2022
DOI : https://doi.org/10.26634/jit.11.2.18800

Abstract

An automated music classification system is that automates the entire process of music classification, i.e., classifying music without any human intervention. Along with this, it is very important to have a good recommendation system that will help with features such as music classification. This paper proposes an automated music classification system that will be very useful for both informational and entertainment purposes in the field of music. This system, based on a musicrelated Artificial Intelligence (AI) algorithm, automatically categorizes different types of music corresponding to different music genres, e.g., Hip Hop, Jazz, Rock, Blues, etc. Another feature added to this system is to recommend similar songs classified by the system. The architecture of the system and the algorithms used at each stage are described and implemented in this paper. The system also provides a lyrics classification module that generates and provides users with lyrics of the user choice. Compared to realistic musical classification, which has always been a difficult problem as it may lack structure or rationality.

Keywords

Python, CNN, Deep Learning, Genre, AI, Residual Neural Network.

How to Cite this Article?

Mishra, A. K., Singh, D. K., and Khare, A. (2022). Music Genre Detection using Deep Learning Models. i-manager’s Journal on Information Technology, 11(2), 10-15. https://doi.org/10.26634/jit.11.2.18800

References

[1]. Bisharad, D., & Laskar, R. H. (2019, October). Music Genre Recognition Using Residual Neural Networks. In TENCON 2019-2019 IEEE Region 10 Conference (TENCON), (pp. 2063-2068). https://doi.org/10.1109/TENCON.2019.8929406

[2]. Chillara, S., Kavitha, A. S., Neginhal, S. A., Haldia, S., & Vidyullatha, K. S. (2019). Music genre classification using machine learning algorithms: A comparison. International Research Journal of Engineering and Technology, 6(5), 851-858.

[3]. Costa, Y. M., Oliveira, L. S., Koericb, A. L., & Gouyon, F. (2011, June). Music genre recognition using spectrograms. In 2011, 18th International Conference on Systems, Signals and Image Processing, 1-4.

[4]. Ghosal, D., & Kolekar, M. H. (2018, September). Music genre recognition using deep neural networks and transfer learning. In Interspeech, (pp. 2087-2091).

[5]. Hafemann, L. G., Oliveira, L. S., & Cavalin, P. (2014, August). Forest species recognition using deep convolutional neural networks. In 2014, 22nd International Conference on Pattern Recognition, 1103-1107. https://doi.org/10.1109/ICPR.2014.199

[6]. Panagakis, Y., & Kotropoulos, C. (2010, March). Music genre classification via topology preserving nonnegative tensor factorization and sparse representations. In 2010, IEEE International Conference on Acoustics, Speech and Signal Processing, 249-252. https://doi.org/10.1109/ICASSP.2010.5495984

[7]. Panagakis, Y., Kotropoulos, C., & Arce, G. R. (2009, August). Music genre classification via sparse representations of auditory temporal modulations. In 2009, 17th European Signal Processing Conference, 1-5.

[8]. Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293-302. https://doi.org/10.1109/TSA.2002.800560

[9]. 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, 5, (pp. 429). https://doi.org/10.1109/ICASSP.2003.1199998

[10]. Zhang, S., Gu, H., & Li, R. (2019). Music Genre Classification: Near-Realtime Vs Sequential Approach. PrePrint.

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