Artificial Intelligence in Music: Applications, Challenges, and Future Prospects
Research Article
Open Access
CC BY

Artificial Intelligence in Music: Applications, Challenges, and Future Prospects

Yichen Zhu 1*
1 Nanjing International School
*Corresponding author: yichenzhu@nanjing-school.com
Published on 28 October 2025
Journal Cover
CHR Vol.92
ISSN (Print): 2753-7072
ISSN (Online): 2753-7064
ISBN (Print): 978-1-80590-481-6
ISBN (Online): 978-1-80590-482-3
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Abstract

In recent years, Artificial Intelligence (AI) has been remarkably progress in many field. Nowadays AI has rapidly expanded its influence into the field of music, introducing innovative approaches to music composition, music education and music performance assessments. This study discusses how AI has been used inside these areas, where assisting human at AI educational purposes and also in composition purposes. This paper also addresses key challenges, including issues of model interpretability, stylistic limitations and other ethical concerns that are related to authorship. Lastly, the paper indicates future directions for AI usage in the music domain. In particular, further research may focus on developing more transparent algorithms to improve user trust, exploring hybrid systems that integrate human creativity with machine intelligence, and establishing clearer frameworks for copyright and ownership of AI-generated works. By providing this structured overview, the review seeks to promote a deeper understanding of AI’s potential as a collaborative tool in reshaping the future of music.

Keywords:

Artificial Intelligence, Music, Machine Learning.

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Zhu,Y. (2025). Artificial Intelligence in Music: Applications, Challenges, and Future Prospects. Communications in Humanities Research,92,6-11.

References

[1]. Ye, G., Liu, Y., Zhou, T., Li, X., & Zhang, Q. (2023). An automatic music generation and evaluation method based on transfer learning. PLOS ONE, 18(5), e0283103.

[2]. Zhou, X. (2023). Analysis of Evaluation in Artificial Intelligence Music. Journal of Artificial Intelligence Practice, 6(8), 6-11.

[3]. Yu, X., Ma, N., Zheng, L., Wang, L., & Wang, K. (2023). Developments and Applications of Artificial Intelligence in Music Education. Technologies, 11(2), 42.

[4]. Civit, M., Civit-Masot, J., Cuadrado, F., & Escalona, M. J. (2022). A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends. Expert Systems with Applications, 209, 118190.

[5]. Swanwick, K. (2002). Musical knowledge: Intuition, analysis and music education. Routledge.

[6]. Omar, R., Hailstone, J. C., Warren, J. E., Crutch, S. J., & Warren, J. D. (2010). The cognitive organization of music knowledge: a clinical analysis. Brain, 133(4), 1200-1213.

[7]. Carroll, C. L. (2020). Seeing the invisible: Theorising connections between informal and formal musical knowledge. Research Studies in Music Education, 42(1), 37-55.

[8]. Johnson, M. L. (2002). Toward an expert system for expressive musical performance. Computer, 24(7), 30-34.

[9]. Raphael, C. (2001). A probabilistic expert system for automatic musical accompaniment. Journal of Computational and Graphical Statistics, 10(3), 487-512.

[10]. Cope, D. (1987). An expert system for computer-assisted composition. Computer Music Journal, 11(4), 30-46.

References

[1]. Ye, G., Liu, Y., Zhou, T., Li, X., & Zhang, Q. (2023). An automatic music generation and evaluation method based on transfer learning. PLOS ONE, 18(5), e0283103.

[2]. Zhou, X. (2023). Analysis of Evaluation in Artificial Intelligence Music. Journal of Artificial Intelligence Practice, 6(8), 6-11.

[3]. Yu, X., Ma, N., Zheng, L., Wang, L., & Wang, K. (2023). Developments and Applications of Artificial Intelligence in Music Education. Technologies, 11(2), 42.

[4]. Civit, M., Civit-Masot, J., Cuadrado, F., & Escalona, M. J. (2022). A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends. Expert Systems with Applications, 209, 118190.

[5]. Swanwick, K. (2002). Musical knowledge: Intuition, analysis and music education. Routledge.

[6]. Omar, R., Hailstone, J. C., Warren, J. E., Crutch, S. J., & Warren, J. D. (2010). The cognitive organization of music knowledge: a clinical analysis. Brain, 133(4), 1200-1213.

[7]. Carroll, C. L. (2020). Seeing the invisible: Theorising connections between informal and formal musical knowledge. Research Studies in Music Education, 42(1), 37-55.

[8]. Johnson, M. L. (2002). Toward an expert system for expressive musical performance. Computer, 24(7), 30-34.

[9]. Raphael, C. (2001). A probabilistic expert system for automatic musical accompaniment. Journal of Computational and Graphical Statistics, 10(3), 487-512.

[10]. Cope, D. (1987). An expert system for computer-assisted composition. Computer Music Journal, 11(4), 30-46.

Cite this article

Zhu,Y. (2025). Artificial Intelligence in Music: Applications, Challenges, and Future Prospects. Communications in Humanities Research,92,6-11.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceeding of ICIHCS 2025 Symposium: Integration & Boundaries: Humanities/Arts, Technology and Communication

ISBN: 978-1-80590-481-6(Print) / 978-1-80590-482-3(Online)
Editor: Enrique Mallen, Cai Yong
Conference website: https://2025.icihcs.org/
Conference date: 17 November 2025
Series: Communications in Humanities Research
Volume number: Vol.92
ISSN: 2753-7064(Print) / 2753-7072(Online)

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