A Review on Music Genre Classification Methods

Authors

  • E Madhusudhana Reddy  Professor, Department of Computer Science and Engineering, Bhoj Reddy Engineering College for Women, Vinay Nagar, Hyderabad-59, Telangana, India
  • Likhitha Vallabhaneni  Department of Computer Science and Engineering, Bhoj Reddy Engineering College for Women, Vinay Nagar, Hyderabad-59, Telangana, India
  • Navya Uppala  Department of Computer Science and Engineering, Bhoj Reddy Engineering College for Women, Vinay Nagar, Hyderabad-59, Telangana, India

Keywords:

Convolutional Neural Networks, Music Streaming Services, Transfer Learning, Deep Learning

Abstract

Music has become an important part in our life. We have an always evolving music industry, producing various songs each day. This study delves into the realm of music genre classification using machine learning techniques, acknowledging the pivotal role music plays in providing relief, entertainment, and emotional expression. The proliferation of music streaming services necessitates accurate classification models, leading to an exploration of various methodologies. The literature survey reviews notable research, highlighting the efficacy of Convolutional Neural Networks (CNNs), deep learning techniques, and Transfer Learning in achieving high classification accuracy.

References

  1. K. S. Mounika, S. Deyaradevi, K. Swetha and V. Vanitha, "Music Genre Classification Using Deep Learning," 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India, 2021, pp. 1-7, doi: 10.1109/ICAECA52838.2021.9675685.
  2. L. K. Puppala, S. S. R. Muvva, S. R. Chinige and P. S. Rajendran, "A Novel Music Genre Classification Using Convolutional Neural Network," 2021 6th International Conference on Communication and Electronics Systems (ICCES), Coimbatre, India, 2021, pp. 1246-1249, doi: 10.1109/ICCES51350.2021.9489022.
  3. Sunil Kumar Prabhakar, Seong-Whan Lee, Holistic Approaches to Music Genre Classification using Efficient Transfer and Deep Learning Techniques, Expert Systems with Applications, Volume 211, 2023, 118636, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2022.118636.
  4. S. Prince, J. J. Thomas, S. J. J, K. P. Priya and J. J. Daniel, "Music Genre Classification using Deep learning - A review," 2022 6th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bangalore, India, 2022, pp. 1-5, doi: 10.1109/CSITSS57437.2022.10026394.
  5. M. S. Rao, O. Pavan Kalyan, N. N. Kumar, M. Tasleem Tabassum and B. Srihari, "Automatic Music Genre Classification Based on Linguistic Frequencies Using Machine Learning," 2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI), Tebessa, Algeria, 2021, pp. 1-5, doi: 10.1109/ICRAMI52622.2021.9585937.
  6. S. Vishnupriya and K. Meenakshi, "Automatic Music Genre Classification using Convolution Neural Network," 2018 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2018, pp. 1-4, doi: 10.1109/ICCCI.2018.8441340. [7]. Music Genre Classification and recommendation using ML techniques
  7. A. Elbir, H. Bilal Çam, M. Emre Iyican, B. Öztürk and N. Aydin, "Music Genre Classification and Recommendation by Using Machine Learning Techniques," 2018 Innovations in Intelligent Systems and Applications Conference (ASYU), Adana, Turkey, 2018, pp. 1-5, doi: 10.1109/ASYU.2018.8554016. [9]. Music Genre Classification using Deep Learning
  8. S. W. J, P. K. R M, P. K. K and P. J, "Music Genre Classification Using LSTM and CNN," 2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN), Salem, India, 2023, pp. 205-209, doi: 10.1109/ICPCSN58827.2023.00039.
  9. W. Suo, "Efficient Music Genre Classification with Deep Convolutional Neural Networks," 2022 5th International Conference on Data Science and Information Technology (DSIT), Shanghai, China, 2022, pp. 01-05, doi: 10.1109/DSIT55514.2022.9943952.
  10. I. Pathania and N. Kaur, "Classification of Music Genre Using Machine Learning," 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT), Bangalore, India, 2022, pp. 1-5, doi: 10.1109/GCAT55367.2022.9972105.
  11. C. -H. Chou and J. -H. Shi, "Time-Frequency Analysis for Music Genre Classification by using Wavelet Package Decompositions," 2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII), Jeju, Korea (South), 2018, pp. 134-137, doi: 10.1109/ICKII.2018.8569119.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2023-12-30

Issue

Volume 9, Issue 6, November-December-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
E Madhusudhana Reddy, Likhitha Vallabhaneni, Navya Uppala, " A Review on Music Genre Classification Methods" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 6, pp.275-279, November-December-2023.