Research on multi-label classification model design in online teaching and learning for music scholars from a biomechanical perspective
Abstract
Music serves as a vital medium for emotional expression and cultural heritage, evolving significantly through advancements in digital education. This study introduces an integrated framework to enhance online music education via innovative music generation and genre classification techniques. Central to this research is the MGED (Music Generation and Education Development) framework, which utilizes Convolutional Neural Networks (CNNs) for feature extraction and Highway Networks for deep learning. By incorporating spatial attention mechanisms with Bidirectional Gated Recurrent Units (Bi-GRU), the framework generates high-fidelity spectrograms, while the Griffin-Lim algorithm ensures temporal coherence in the outputs. For genre classification, the study employs the CMBRU (Classification Model for Bi-GRU and Residual Units) framework, leveraging Mel-Frequency Cepstral Coefficients (MFCC) and multi-channel CNNs to achieve robust representation learning. This model effectively captures temporal dependencies, resulting in over 70% accuracy across five genres. Additionally, this research explores the design of a multi-label classification model tailored for online teaching and learning environments aimed at music scholars, viewed through a biomechanical lens. As online education becomes increasingly prevalent, the need for effective classification models that can handle multiple labels simultaneously is critical, particularly in music education, where diverse skills and knowledge areas intersect. The study employs biomechanical principles to analyze the physical aspects of music performance and learning, integrating these insights into the classification model. This approach not only enhances the educational experience for music scholars but also contributes to the broader field of online music education, paving the way for future research and practical applications.
References
1. Kholjurayevich MN, Madaminovich FK. The role of music culture in inculcating the idea of national independence in the minds of the younger generation. ACADEMICIA: An International Multidisciplinary Research Journal. 2021; 11(5): 71–74. doi: 10.5958/2249-7137.2021.01351.3
2. Connolly R, D’Acierno P. Italian American Musical Culture and Its Contribution to American Music. The Italian American Heritage; 2021.
3. Ndou N, Ajoodha R, Jadhav A. Music Genre Classification: A Review of Deep-Learning and Traditional Machine-Learning Approaches. In: Proceedings of the 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS); 2021.
4. Jamshidi F, Marghitu D, Chapman R. Developing an online music teaching and practicing platform via machine learning: a review paper. Cham: Springer International Publishing; 2021.
5. Xia Y, Xu F. Design and Application of Machine Learning-Based Evaluation for University Music Teaching. Mathematical Problems in Engineering. 2022; 2022: 1–10. doi: 10.1155/2022/4081478
6. Verma P, Chafe C. A Generative Model for Raw Audio Using Transformer Architectures. In: Proceedings of the 2021 24th International Conference on Digital Audio Effects (DAFx); 2021.
7. Mehri S, Kumar K, Gulrajani I, et al. SampleRNN: An unconditional end-to-end neural audio generation model. arXiv; 2019.
8. Donahue C, McAuley J, Puckette M. Synthesizing audio with generative adcersarial networks. In: Proceedings of the 10th International Conference on Learning Representations conference; 2019.
9. Subakan C, Ravanelli M, Cornell S, et al. Attention Is All You Need In Speech Separation. IEEE; 2021.
10. Zhu H, Liu Q, Yuan NJ, et al. XiaoIce Band: A melody and arrangement generation framework for pop music. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining; 2018.
11. Tsushima H, Nakamura E, Itoyama K, et al. Interactive arrangement of chords and melodies based on a tree-structured generative model. In: Proceedings of the International Society for Music Information Retrieval Conference; 2018.
12. Dineley J, Carr E, White LL, et al. Variability of speech timing features across repeated recordings: a comparison of open-source extraction techniques. In: Proceedings of the Interspeech 2024; 1–5 September 2024; Kos, Greece.
13. Cheng YH, Chang PC, Kuo CN. Convolutional Neural Networks Approach for Music Genre Classification. IEEE; 2020.
14. Sugianto S, Suyanto S. Voting-Based Music Genre Classification Using Melspectogram and Convolutional Neural Network. In: Proceedings of the 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI); 2019.
15. Atahan Y, Elbir A, Enes Keskin A, et al. Music Genre Classification Using Acoustic Features and Autoencoders. In: Proceedings of the 2021 Innovations in Intelligent Systems and Applications Conference (ASYU); 2021.
16. Liu X, Song S, Zhang M, et al. MATT. A Multiple-instance Attention Mechanism for Long-tail Music Genre Classification. In: Proceedings of the 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2022.
17. Prabhakar SK, Lee SW. Holistic Approaches to Music Genre Classification using Efficient Transfer and Deep Learning Techniques. Expert Systems with Applications. 2023; 211: 118636. doi: 10.1016/j.eswa.2022.118636
18. Hasib KMd, Tanzim A, Shin J, et al. BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features. IEEE Access. 2022; 10: 108545–108563. doi: 10.1109/access.2022.3213818
19. Sharma G, Umapathy K, Krishnan S. Trends in audio signal feature extraction methods. Applied Acoustics. 2020; 158: 107020. doi: 10.1016/j.apacoust.2019.107020
20. Demir F, Turkoglu M, Aslan M, et al. A new pyramidal concatenated CNN approach for environmental sound classification. Applied Acoustics. 2020; 170: 107520. doi: 10.1016/j.apacoust.2020.107520
21. Shahriar S. GAN computers generate arts? A survey on visual arts, music, and literary text generation using generative adversarial network. Displays. 2022; 73: 102237. doi: 10.1016/j.displa.2022.102237
22. Masuyama Y, Yatabe K, Koizumi Y, et al. Deep Griffin–Lim Iteration: Trainable Iterative Phase Reconstruction Using Neural Network. IEEE Journal of Selected Topics in Signal Processing. 2021; 15(1): 37–50. doi: 10.1109/jstsp.2020.3034486
23. SuriyaPrakash J, Kiran S. Obtain Better Accuracy Using Music Genre Classification Systemon GTZAN Dataset. In: Proceedings of the 2022 IEEE North Karnataka Subsection Flagship International Conference (NKCon); 2022.
24. Edwards D, Dixon S, Benetos E, et al. A Data-Driven Analysis of Robust Automatic Piano Transcription. IEEE Signal Processing Letters. 2024; 31: 681–685. doi: 10.1109/lsp.2024.3363646
25. Ghildiyal A, Singh K, Sharma S. Music Genre Classification using Machine Learning. In: Proceedings of the 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA); 2020.
Copyright (c) 2025 Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
