mlsanityData to 14 Oct 2026

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

arXiv 2509.23364 ↗First posted 27 Sep 2025eess.AS cs.LG cs.SDcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 29 of its 50 references that are in the map. First posted on arXiv 27 Sep 2025.

Builds on

29 of 50 references.

References · 29

  1. 2022AudioLM: a Language Modeling Approach to Audio Generationcited by 616
  2. 2023Simple and Controllable Music Generationcited by 488
  3. 2023MusicLM: Generating Music From Textcited by 448
  4. 2023AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretrainingcited by 344
  5. 2022AudioGen: Textually Guided Audio Generationcited by 295
  6. 2022Diffsound: Discrete Diffusion Model for Text-to-sound Generationcited by 257
  7. 2024Stable Audio Opencited by 197
  8. 2022Hybrid Transformers for Music Source Separationcited by 134
  9. 2023Adapting Frechet Audio Distance for Generative Music Evaluationcited by 92
  10. 2021Hybrid Spectrogram and Waveform Source Separationcited by 91
  11. 2023Music ControlNet: Multiple Time-varying Controls for Music Generationcited by 83
  12. 2019Demucs: Deep Extractor for Music Sources with extra unlabeled data remixedcited by 54
17 more
  1. 2024Masked Audio Generation using a Single Non-Autoregressive Transformercited by 50
  2. 2024Foundation Models for Music: A Surveycited by 37
  3. 2024MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Modelscited by 32
  4. 2024Joint Audio and Symbolic Conditioning for Temporally Controlled Text-to-Music Generationcited by 28
  5. 2020D3Net: Densely connected multidilated DenseNet for music source separationcited by 24
  6. 2023Toward General Design Principles for Generative AI Applicationscited by 21
  7. 2025MusicGen-Stem: Multi-stem music generation and edition through autoregressive modelingcited by 12
  8. 2024MusiConGen: Rhythm and Chord Control for Transformer-Based Text-to-Music Generationcited by 11
  9. 2025Exploring the Collaborative Co-Creation Process with AI: A Case Study in Novice Music Productioncited by 9
  10. 2024A Stem-Agnostic Single-Decoder System for Music Source Separation Beyond Four Stemscited by 7
  11. 2024PAGURI: a user experience study of creative interaction with text-to-music modelscited by 4
  12. 2023IteraTTA: An interface for exploring both text prompts and audio priors in generating music with text-to-audio modelscited by 3
  13. 2024Between the AI and Me: Analysing Listeners' Perspectives on AI- and Human-Composed Progressive Metal Musiccited by 3
  14. 2024MambaFoley: Foley Sound Generation using Selective State-Space Modelscited by 3
  15. 2025Mitigating data replication in text-to-audio generative diffusion models through anti-memorization guidancecited by 2
  16. 2024The Interpretation Gap in Text-to-Music Generation Modelscited by 1
  17. 2025Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classificationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026MusGU+: Toward a Musician-Centered Evaluation Framework and Discovery Tool for Generative Music AIcited by 1