Research Article | | Peer-Reviewed

The Application Categories and Technical Frameworks of Artificial Intelligence Technologies in Higher Education Music Composition Instruction

Received: 6 November 2023     Accepted: 28 November 2023     Published: 29 November 2023
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Abstract

Music composition, a pivotal facet of higher education music instruction, intricately weaves together creative and technical elements. Artificial intelligence technologies have wielded a transformative influence on this domain, introducing innovations that profoundly enhance the landscape of music composition education in universities. This paper commences with an exhaustive classification of the application categories of artificial intelligence technologies in higher education music composition instruction. These encompass intelligent music education platforms, automated assessment and feedback systems, tools aiding music composition, algorithms facilitating music generation, the development of teaching materials and resources, and tools for music recommendation. The subsequent exploration delves meticulously into the nuanced technical architectures underpinning each distinct application category. The primary objective of this paper is to furnish profound insights into the expansive realm of higher education music composition, shedding light on how artificial intelligence technologies have ushered in unprecedented possibilities for music learning and creation. By providing an in-depth comprehension of application categories and their associated technical architectures, this paper stands as an invaluable reference, poised to inform and guide future practices and research endeavors within the domain of music composition education. The comprehensive elucidation herein aims to bridge the gap between traditional pedagogies and cutting-edge technological advancements, thereby enriching the discourse on the future of music composition education in higher institutions.

Published in Higher Education Research (Volume 8, Issue 6)
DOI 10.11648/j.her.20230806.14
Page(s) 232-241
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Artificial Intelligence, Higher Education, Music Composition

References
[1] Carnovalini, F., & Rodà, A. Computational creativity and music generation systems: An introduction to the state of the art. Frontiers in Artificial Intelligence, 3, 14, 2020, pp. 1-13.
[2] Chen, X. Research and application of interactive teaching music intelligent system based on artificial intelligence. In International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021), December 2021, Vol. 12153, p. 1215302.
[3] Ji, S., Yang, X. & Luo, J. A Survey on Deep Learning for Symbolic Music Generation: Representations, Algorithms, Evaluations, and Challenges. ACM Comput. Surv. 56, 1, 2023, pp. 100-139.
[4] Kaliakatsos-Papakostas, M., Floros, A., & Vrahatis, M. N. Chapter 13-Artificial intelligence methods for music generation: a review and future perspectives, Algorithms, Theory and Applications, 2020, pp. 217-245.
[5] Li, P. P., & Wang, B. Artificial Intelligence in Music Education. International Journal of Human–Computer Interaction, 2023, pp. 1-10.
[6] Majidi, M., & Toroghi, R. M. A combination of multi-objective genetic algorithm and deep learning for music harmony generation. Multimedia Tools and Applications, 82 (2), 2023, pp. 2419-2435.
[7] Mao, H. H., Shin, T., & Cottrell, G. Deep J: Style-specific music generation. In 2018 IEEE 12th International Conference on Semantic Computing (ICSC). January 2018. pp. 377-382.
[8] Shang, M. The application of artificial intelligence in music education. In Intelligent Computing Theories and Application: 15th International Conference, ICIC 2019, Nanchang, China, August 3-6, 2019, pp. 662-668.
[9] Smith, B. Artificial intelligence and music education. In Readings in Music and Artificial Intelligence, 2013, pp. 221-237.
[10] Wang, S., Sun, Z., & Chen, Y. Effects of higher education institutes’ artificial intelligence capability on students’ self-efficacy, creativity and learning performance, Education and Information Technologies, vol. 28, 2023, pp. 4919–4939.
[11] Wang, T. The rise of big data on the development of music education and innovation to promote. Applied Mathematics and Nonlinear Sciences. 09, 2023, pp. 164-167.
[12] Wei, J., Karuppiah, M., & Prathik, A. College music education and teaching based on AI techniques. Computers and Electrical Engineering, 100, 2022, pp. 107851.
[13] Xu, D., Xu, H. Application of genetic algorithm in model music composition innovation, Applied Mathematics and Nonlinear Sciences, vol. 07, 2023, pp. 12-24.
[14] Yang, F. Artificial intelligence in music education. In 2020 International Conference on Robots & Intelligent System (ICRIS), November 2020, pp. 483-484.
[15] Yang, L. C., & Lerch, A. On the evaluation of generative models in music. Neural Computing and Applications, 32 (9), 2020, pp. 4773-4784.
[16] Yu, X., Ma, N., Zheng, L., Wang, L., & Wang, K. Developments and applications of artificial intelligence in music education. Technologies, 11 (2), 2023, pp. 42.
Cite This Article
  • APA Style

    Li, N. (2023). The Application Categories and Technical Frameworks of Artificial Intelligence Technologies in Higher Education Music Composition Instruction. Higher Education Research, 8(6), 232-241. https://doi.org/10.11648/j.her.20230806.14

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    ACS Style

    Li, N. The Application Categories and Technical Frameworks of Artificial Intelligence Technologies in Higher Education Music Composition Instruction. High. Educ. Res. 2023, 8(6), 232-241. doi: 10.11648/j.her.20230806.14

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    AMA Style

    Li N. The Application Categories and Technical Frameworks of Artificial Intelligence Technologies in Higher Education Music Composition Instruction. High Educ Res. 2023;8(6):232-241. doi: 10.11648/j.her.20230806.14

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  • @article{10.11648/j.her.20230806.14,
      author = {Ning Li},
      title = {The Application Categories and Technical Frameworks of Artificial Intelligence Technologies in Higher Education Music Composition Instruction},
      journal = {Higher Education Research},
      volume = {8},
      number = {6},
      pages = {232-241},
      doi = {10.11648/j.her.20230806.14},
      url = {https://doi.org/10.11648/j.her.20230806.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.her.20230806.14},
      abstract = {Music composition, a pivotal facet of higher education music instruction, intricately weaves together creative and technical elements. Artificial intelligence technologies have wielded a transformative influence on this domain, introducing innovations that profoundly enhance the landscape of music composition education in universities. This paper commences with an exhaustive classification of the application categories of artificial intelligence technologies in higher education music composition instruction. These encompass intelligent music education platforms, automated assessment and feedback systems, tools aiding music composition, algorithms facilitating music generation, the development of teaching materials and resources, and tools for music recommendation. The subsequent exploration delves meticulously into the nuanced technical architectures underpinning each distinct application category. The primary objective of this paper is to furnish profound insights into the expansive realm of higher education music composition, shedding light on how artificial intelligence technologies have ushered in unprecedented possibilities for music learning and creation. By providing an in-depth comprehension of application categories and their associated technical architectures, this paper stands as an invaluable reference, poised to inform and guide future practices and research endeavors within the domain of music composition education. The comprehensive elucidation herein aims to bridge the gap between traditional pedagogies and cutting-edge technological advancements, thereby enriching the discourse on the future of music composition education in higher institutions.
    },
     year = {2023}
    }
    

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    AB  - Music composition, a pivotal facet of higher education music instruction, intricately weaves together creative and technical elements. Artificial intelligence technologies have wielded a transformative influence on this domain, introducing innovations that profoundly enhance the landscape of music composition education in universities. This paper commences with an exhaustive classification of the application categories of artificial intelligence technologies in higher education music composition instruction. These encompass intelligent music education platforms, automated assessment and feedback systems, tools aiding music composition, algorithms facilitating music generation, the development of teaching materials and resources, and tools for music recommendation. The subsequent exploration delves meticulously into the nuanced technical architectures underpinning each distinct application category. The primary objective of this paper is to furnish profound insights into the expansive realm of higher education music composition, shedding light on how artificial intelligence technologies have ushered in unprecedented possibilities for music learning and creation. By providing an in-depth comprehension of application categories and their associated technical architectures, this paper stands as an invaluable reference, poised to inform and guide future practices and research endeavors within the domain of music composition education. The comprehensive elucidation herein aims to bridge the gap between traditional pedagogies and cutting-edge technological advancements, thereby enriching the discourse on the future of music composition education in higher institutions.
    
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Author Information
  • Department of Music, Shenzhen University, Shenzhen, China

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