An Empirical Study Comparing the AI Music Generation Model with Electronic Music Composition Methods: a Multidimensional Analysis of Compositional Process, Quality and Efficiency

DOI

10.2991/978-2-38476-432-7_22How to use a DOI?

Keywords

AI music generation; electronic music composition; creative workflow; output quality

Abstract

This study is concerned with AI-based music generation tools like OpenAI and Jukedeck as compared to music production techniques like Ableton Live. It examines these two types of production processes. The research offered a variety of composition tasks -from melody generation to building a song- as well as asked evaluations from experts and common at large audiences. It was revealed that experts and listeners revealed peculiar plusses and minuses of each developed method. Although AI tools are superior in terms of speed, electronic music production provides more space to the sound innovator since the art depth here goes along maximizing musicians’ crave for originality. These insights not only guide musicians in selecting appropriate tools but also underscore the synergistic potential of combining AI and human creativity. Besides that, the research advances the discussion of “technology-enhanced art”, a field that is being rooted to replace the traditional music model by the digital transformation. The developments may focus on hybrid workflows where AI inputs are nourished with human contributions, prompting innovative music creativity.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yang Bai
PY  - 2025
DA  - 2025/06/22
TI  - An Empirical Study Comparing the AI Music Generation Model with Electronic Music Composition Methods: a Multidimensional Analysis of Compositional Process, Quality and Efficiency
BT  - Proceedings of the 2025 4th International Conference on Social Sciences and Humanities and Arts (SSHA 2025)
PB  - Atlantis Press
SP  - 198
EP  - 205
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-432-7_22
DO  - 10.2991/978-2-38476-432-7_22
ID  - Bai2025
ER  -