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

Ning Li

doi:doi:10.11648/j.her.20230806.14

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

Ning Li

Published in Higher Education Research (Volume 8, Issue 6)

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), 2024. Published by Science Publishing Group

Keywords

Artificial Intelligence, Higher Education, Music Composition

References

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[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.
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[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.

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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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TY  - JOUR
T1  - The Application Categories and Technical Frameworks of Artificial Intelligence Technologies in Higher Education Music Composition Instruction
AU  - Ning Li
Y1  - 2023/11/29
PY  - 2023
N1  - https://doi.org/10.11648/j.her.20230806.14
DO  - 10.11648/j.her.20230806.14
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EP  - 241
PB  - Science Publishing Group
SN  - 2578-935X
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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.

VL  - 8
IS  - 6
ER  -

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Author Information

Ning Li

Department of Music, Shenzhen University, Shenzhen, China

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Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
T1  - The Application Categories and Technical Frameworks of Artificial Intelligence Technologies in Higher Education Music Composition Instruction
AU  - Ning Li
Y1  - 2023/11/29
PY  - 2023
N1  - https://doi.org/10.11648/j.her.20230806.14
DO  - 10.11648/j.her.20230806.14
T2  - Higher Education Research
JF  - Higher Education Research
JO  - Higher Education Research
SP  - 232
EP  - 241
PB  - Science Publishing Group
SN  - 2578-935X
UR  - https://doi.org/10.11648/j.her.20230806.14
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.

VL  - 8
IS  - 6
ER  -

Copy | Download