Music Genre Classification and Recommendation

Authors

  • Prof. Rahul Ghode  Professor at Information Technology Department, Dhole Patil College of Engineering, Wagholi, Pune, Maharashtra, India
  • Pranav Navale  B. E. Scholar, Information Technology Department, Dhole Patil College of Engineering, Wagholi, Pune, Maharashtra, India
  • Mayur Jadhav  B. E. Scholar, Information Technology Department, Dhole Patil College of Engineering, Wagholi, Pune, Maharashtra, India
  • Anirudha Chippa  B. E. Scholar, Information Technology Department, Dhole Patil College of Engineering, Wagholi, Pune, Maharashtra, India
  • Minal Bhandare  B. E. Scholar, Information Technology Department, Dhole Patil College of Engineering, Wagholi, Pune, Maharashtra, India

DOI:

https://doi.org/10.32628/CSEIT217684

Keywords:

Artificial Intelligence, Genre, Neural organization, Harcascade, Hyper-plane, Support Vector Machines

Abstract

There are various sorts to group the music. Classes are for the most part various classifications wherein music is partitioned. In this day and age as music industry develops quickly, there are various kinds of music sorts made. It is essential to classify the music into these classifications, yet it is mind boggling task. In past times this is done physically and prerequisite for programmed framework for type grouping emerges. As a rule, AI techniques are utilized to group music types and profound learning strategy is utilized to prepare the model yet in this undertaking, we will utilize neural organization strategies for the characterization.

References

  1. S L Happy and Aurobinda Routray, “Automatic Facial Expression Recognition using Features of salient Facial Patches,” in IEEE Trans. On Affective Computing, January-March 2015, pp. 1-12.
  2. Hafeez Kabani, Sharik Khan, Omar Khan and Shabana Tadvi, “Emotion based Music Player,” Int. J. of Eng. Research and General Sci., Vol. 3, Issue 1, pp. 750-756, January-February 2015.
  3. Li Siquan, Zhang Xuanxiong, “Research on Facial Expression Recognition Based on Convolutional Neural Networks”, Journal of Software, 2018, v.17; No.183 (01): 32-35.
  4. Hou Yuqingyang, Quan Jicheng, Wang Hongwei, “Overview of the development of deep learning”, Ship Electronic Engineering, 2017, 4: 5-9.
  5. Liu Sijia, Chen Zhikun, Wang Fubin, “Multi-angle face recognition based on convolutional neural network”, Journal of North China University of Technology (Natural Science Edition), 2019, 41 (4): 103-108.
  6. Li Huihui, “Research on facial expression recognition based on cognitive machine learning”, Guangzhou: South China University of Technology, 2019.
  7. Li Yong, Lin Xiaozhu, Jiang Mengying, “Facial expression recognition based on cross-connection”, LeNet-5 network J], Journal of Automation, 2018,44 (1): 176-182.
  8. Yao L S, Xu G M, Zhap F, “Facial Expression Recognition Based on CNN Local Feature Fusion”, Laser and Optoelectronics Progress, 2020, 57(03): 032501.
  9. Xie S, Hu H., “Facial expression recognition with FRR-CNN”, Electronics Letters, 2017, 53 (4): 235-237.
  10. Nikki Pelchat, Craig M Gelowitz, “Neural Network Music Genre Classification”, 2019.
  11. Vishnupriya S, K. Meenakshi, “Automatic Music Genre Classification using Convolution Neural Network”, International Conference on Computer Communication and Informatics (ICCCI -2017), Coimbatore, 2018.
  12. George Tzanetakis and Perry Cook, “Musical Genre Classification of Audio Signals”, IEEE Transactions on Speech and Audio Processing, VOL. 10, NO. 5, JULY 2002.
  13. Rajeeva Shreedhara Bhat, Rohit B. R., Mamatha K. R., “Music Genre Classification”, SSRG International Journal of Communication and Media Science (SSRG-IJCMS) – Volume 7 Issue 1 – Jan - April 2020.
  14. Kaichun K. Chang, Jyh-Shing Roger Jang, Costas S. Iliopoulos, “Music Genre Classification Via Compressive Sampling”, 11th International Society for Music Information Retrieval Conference.
  15. Beici Liang, Minwei Gu, “Music Genre Classification Using Transfer Learning”, IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 2020.

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

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Published

2021-12-30

Issue

Volume 7, Issue 6, November-December-2021

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]
Prof. Rahul Ghode, Pranav Navale, Mayur Jadhav, Anirudha Chippa, Minal Bhandare, " Music Genre Classification and Recommendation" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 6, pp.298-301, November-December-2021. Available at doi : https://doi.org/10.32628/CSEIT217684