AI-Assisted Music Production: A User Study on Text-to-Music Models

Francesca Ronchini, Luca Comanducci, Simone Marcucci, Fabio Antonacci
9/27/2025
eess.AScs.LGcs.SD

No official code found. Best implementation: lucacoma/AiMusicProductionUserStudy (0 stars, Apache-2.0, last commit 2025-10-23).

Abstract

Text-to-music models have revolutionized the creative landscape, offering new possibilities for music creation. Yet their integration into musicians workflows remains underexplored. This paper presents a case study on how TTM models impact music production, based on a user study of their effect on producers creative workflows. Participants produce tracks using a custom tool combining TTM and source separation models. Semi-structured interviews and thematic analysis reveal key challenges, opportunities, and ethical considerations. The findings offer insights into the transformative potential of TTMs in music production, as well as challenges in their real-world integration.

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Code Implementations(1)

lucacoma/AiMusicProductionUserStudyCommunity65%
Apache-2.0

Repository for the paper Ronchini, F., Comanducci, L., Marcucci, S. & Antonacci, F. AI-Assisted Music Production: A User Study on Text-to- Music Models, accepted for 17th International Symposium on Computer Music Multidisciplinary Research, 3-7 November 2025, London, UK

00PythonSep 25, 202512 months agoApache-2.0

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Cite this paper

@article{ronchini2025aiassisted,
  title  = {AI-Assisted Music Production: A User Study on Text-to-Music Models},
  author = {Francesca Ronchini and Luca Comanducci and Simone Marcucci and Fabio Antonacci},
  year   = {2025},
  eprint = {2509.23364},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.23364v1}
}

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