PDF

Published

2024-11-29

Issue

Vol. 9 No. 11 (2024): Published

Section

Research Articles

License

The journal adopts the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), which means that anyone can reuse and redistribute the materials for non-commercial purposes as long as you follow the license terms and the original source is properly cited.

Author(s) shall retain the copyright of their work and grant the Journal/Publisher rights for the first publication with the work concurrently licensed since 2023 Vol.8 No.2.

Under this license, author(s) will allow third parties to download, reuse, reprint, modify, distribute and/or copy the content under the condition that the authors are given credit. No permission is required from the authors or the publisher.

This broad license intends to facilitate free access, as well as the unrestricted use of original works of all types. This ensures that the published work is freely and openly available in perpetuity.

By providing open access, the following benefits are brought about:

  • Higher Visibility, Availability and Citations-free and unlimited accessibility of the publication over the internet without any restrictions increases citation of the article.
  • Ease of search-publications are easily searchable in search engines and indexing databases.
  • Rapid Publication – accepted papers are immediately published online.
  • Available for free download immediately after publication at https://esp.as-pub.com/index.php/ESP

 

Copyright Statement

1.The authors certify that the submitted manuscripts are original works, do not infringe the rights of others, are free from academic misconduct and confidentiality issues, and that there are no disputes over the authorship scheme of the collaborative articles. In case of infringement, academic misconduct and confidentiality issues, as well as disputes over the authorship scheme, all responsibilities will be borne by the authors.

2. The author agrees to grant the Editorial Office of Environment and Social Psychology a licence to use the reproduction right, distribution right, information network dissemination right, performance right, translation right, and compilation right of the submitted manuscript, including the work as a whole, as well as the diagrams, tables, abstracts, and any other parts that can be extracted from the work and used in accordance with the characteristics of the journal. The Editorial Board of Environment and Social Psychology has the right to use and sub-licence the above mentioned works for wide dissemination in print, electronic and online versions, and, in accordance with the characteristics of the periodical, for the period of legal protection of the property right of the copyright in the work, and for the territorial scope of the work throughout the world.

3. The authors are entitled to the copyright of their works under the relevant laws of Singapore, provided that they do not exercise their rights in a manner prejudicial to the interests of the Journal.

About Licence

Environment and Social Psychology is an open access journal and all published work is available under the Creative Commons Licence, Authors shall retain copyright of their work and grant the journal/publisher the right of first publication, and their work shall be licensed under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).

Under this licence, the author grants permission to third parties to download, reuse, reprint, modify, distribute and/or copy the content with attribution to the author. No permission from the author or publisher is required.

This broad licence is intended to facilitate free access to and unrestricted use of original works of all kinds. This ensures that published works remain free and accessible in perpetuity. Submitted manuscripts, once accepted, are immediately available to the public and permanently accessible free of charge on the journal’s official website (https://esp.as-pub.com/index.php/ESP). Allowing users to read, download, copy, print, search for or link to the full text of the article, or use it for other legal purposes. However, the use of the work must retain the author's signature, be limited to non-commercial purposes, and not be interpretative.

Click to download <Agreement on the Licence for the Use of Copyright on Environmental and Social Psychology>.

How to Cite

Gao, Q., Cai, J., Wang, F., Chang, J., & Huang, H. (2024). Analyzing AI composition techniques and their influence on human musical aesthetics using bi-GRU and self-attention models. Environment and Social Psychology, 9(11). https://doi.org/10.59429/esp.v9i11.3081

Analyzing AI composition techniques and their influence on human musical aesthetics using bi-GRU and self-attention models

Qi Gao

Graduate School of Global Culture Convergence,Kangwon National University,Chuncheon City,Gangwon Province,24341,South Korea

Jinting Cai

Graduate School of Global Culture Convergence,Kangwon National University,Chuncheon City,Gangwon Province,24341,South Korea

Fuxin Wang

Jiangxi Agricultural University, Nanchang, Jiangxi, 330045, China

Junsong Chang

College of Art, Kyung Hee University, Seoul Special City, Seoul Special City, 02447, South Korea

He Huang

College of Music, Qinghai Normal University, Xining, Qinghai Province, 810000, China


DOI: https://doi.org/10.59429/esp.v9i11.3081


Keywords: beat point position, music characteristics, automatic composition, music fluency, musical composition

Abstract

With the continuous development of artificial intelligence technology, this study aims to explore the application of artificial intelligence in human aesthetics. By processing the signal in sub frames and using short-time Fourier transform to analyze the position information of beat points, the start and key musical features of notes can be accurately detected. Based on the extracted music features, a Bi GRU network and self-attention mechanism automatic composition model are established to process important information between longer sequence predictions and prominent notes, and to evaluate the accuracy and vividness of AI composed music works. The results showed that the model achieved an accuracy of 94.28% in processing melody and rhythm data. Excellent performance in terms of music fluency and coordination, with high scores in human music aesthetics indicators, reaching a pitch score of 92, and classical style scores of 90 and 92 in melody and integrity. Artificial intelligence has to some extent influenced and shaped human music aesthetics, providing important evidence for understanding its impact on music creation.

References

[1]. Shi, N., & Wang, Y. (2020). Symmetry in computer-aided music composition system with social network analysis and artificial neural network methods. Journal of Ambient Intelligence and Humanized Computing, 1-16.

[2]. Zhu, H., Liu, Q., Yuan, N. J., Zhang, K., Zhou, G., & Chen, E. (2020). Pop music generation: From melody to multi-style arrangement. ACM Transactions on Knowledge Discovery from Data (TKDD), 14(5), 1-31.

[3]. Jin, H., & Yang, J. (2021). Using computer-aided design software in teaching environmental art design. Computer-Aided Design and Applications, 19(S1), 173-183.

[4]. Li, H. (2020). Piano automatic computer composition by deep learning and blockchain technology. IEEE Access, 8, 188951-188958.

[5]. Hong, J. W., Fischer, K., Ha, Y., & Zeng, Y. (2022). Human, I wrote a song for you: An experiment testing the influence of machines’ attributes on the AI-composed music evaluation. Computers in Human Behavior, 131, 107239.

[6]. Pavlenko, O., Shcherbak, I., Viktoriia, H. U. R. A., Lihus, V., Maidaniuk, I., & Skoryk, T. (2022). Development of music education in virtual and extended reality. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 13(3), 308-319.

[7]. Gioti, A. M. (2020). From artificial to extended intelligence in music composition. Organised Sound, 25(1), 25-32.

[8]. Morreale, F. (2021). Where does the buck stop? Ethical and political issues with AI in music creation.

[9]. Pachet, F., Roy, P., & Carré, B. (2021). Assisted music creation with flow machines: towards new categories of new. Handbook of artificial intelligence for music: Foundations, advanced approaches, and developments for creativity, 485-520.

[10]. Dai, D. D. (2021). Artificial intelligence technology assisted music teaching design. Scientific programming, 2021(1), 9141339.

[11]. Deruty, E., Grachten, M., Lattner, S., Nistal, J., & Aouameur, C. (2022). On the development and practice of ai technology for contemporary popular music production. Transactions of the International Society for Music Information Retrieval, 5(1), 35-50.

[12]. Briot, J. P. (2021). From artificial neural networks to deep learning for music generation: history, concepts and trends. Neural Computing and Applications, 33(1), 39-65.

[13]. Huertas-Abril, C. A. , & Palacios-Hidalgo, F. J. . (2023). Lgbtiq+ education for making teaching inclusive? voices of teachers from all around the world. Environment and Social Psychology.

[14]. Sircar, A., Yadav, K., Rayavarapu, K., Bist, N., & Oza, H. (2021). Application of machine learning and artificial intelligence in oil and gas industry. Petroleum Research, 6(4), 379-391.

[15]. Weng, S. S., & Chen, H. C. (2020). Exploring the role of deep learning technology in the sustainable development of the music production industry. Sustainability, 12(2), 625.

[16]. Shukla, S. (2023). Creative Computing and Harnessing the Power of Generative Artificial Intelligence. Journal Environmental Sciences And Technology, 2(1), 556-579.

[17]. Dash, A., & Agres, K. (2024). AI-Based Affective Music Generation Systems: A Review of Methods and Challenges. ACM Computing Surveys, 56(11), 1-34.

[18]. McCormack, J., Hutchings, P., Gifford, T., Yee-King, M., Llano, M. T., & D’inverno, M. (2020). Design considerations for real-time collaboration with creative artificial intelligence. Organised Sound, 25(1), 41-52.

[19]. Hernandez-Olivan, C., & Beltran, J. R. (2022). Music composition with deep learning: A review. Advances in speech and music technology: computational aspects and applications, 25-50.

[20]. Loughran, R., & O’Neill, M. (2020). Evolutionary music: applying evolutionary computation to the art of creating music. Genetic Programming and Evolvable Machines, 21, 55-85.

[21]. Ijiga, O. M., Idoko, I. P., Enyejo, L. A., Akoh, O., Ugbane, S. I., & Ibokette, A. I. (2024). Harmonizing the voices of AI: Exploring generative music models, voice cloning, and voice transfer for creative expression. World Journal of Advanced Engineering Technology and Sciences, 11(1), 372-394.



21 Woodlands Close #02-10 Primz Bizhub Singapore 737854

Email:editorial_office@as-pub.com