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

arXiv: https://arxiv.org/abs/2509.23364 · HTML: https://mlsanity.com/paper/2509.23364/

## Key facts

- Cited by 1 papers 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 (most cited first, 29 of 29)

- [AudioLM: a Language Modeling Approach to Audio Generation](https://mlsanity.com/paper/2209.03143/) (2022)
- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://mlsanity.com/paper/2308.05734/) (2023)
- [AudioGen: Textually Guided Audio Generation](https://mlsanity.com/paper/2209.15352/) (2022)
- [Diffsound: Discrete Diffusion Model for Text-to-sound Generation](https://mlsanity.com/paper/2207.09983/) (2022)
- [Stable Audio Open](https://mlsanity.com/paper/2407.14358/) (2024)
- [Hybrid Transformers for Music Source Separation](https://mlsanity.com/paper/2211.08553/) (2022)
- [Adapting Frechet Audio Distance for Generative Music Evaluation](https://mlsanity.com/paper/2311.01616/) (2023)
- [Hybrid Spectrogram and Waveform Source Separation](https://mlsanity.com/paper/2111.03600/) (2021)
- [Music ControlNet: Multiple Time-varying Controls for Music Generation](https://mlsanity.com/paper/2311.07069/) (2023)
- [Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed](https://mlsanity.com/paper/1909.01174/) (2019)
- [Masked Audio Generation using a Single Non-Autoregressive Transformer](https://mlsanity.com/paper/2401.04577/) (2024)
- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models](https://mlsanity.com/paper/2402.06178/) (2024)
- [Joint Audio and Symbolic Conditioning for Temporally Controlled Text-to-Music Generation](https://mlsanity.com/paper/2406.10970/) (2024)
- [D3Net: Densely connected multidilated DenseNet for music source separation](https://mlsanity.com/paper/2010.01733/) (2020)
- [Toward General Design Principles for Generative AI Applications](https://mlsanity.com/paper/2301.05578/) (2023)
- [MusicGen-Stem: Multi-stem music generation and edition through autoregressive modeling](https://mlsanity.com/paper/2501.01757/) (2025)
- [MusiConGen: Rhythm and Chord Control for Transformer-Based Text-to-Music Generation](https://mlsanity.com/paper/2407.15060/) (2024)
- [Exploring the Collaborative Co-Creation Process with AI: A Case Study in Novice Music Production](https://mlsanity.com/paper/2501.15276/) (2025)
- [A Stem-Agnostic Single-Decoder System for Music Source Separation Beyond Four Stems](https://mlsanity.com/paper/2406.18747/) (2024)
- [PAGURI: a user experience study of creative interaction with text-to-music models](https://mlsanity.com/paper/2407.04333/) (2024)
- [IteraTTA: An interface for exploring both text prompts and audio priors in generating music with text-to-audio models](https://mlsanity.com/paper/2307.13005/) (2023)
- [Between the AI and Me: Analysing Listeners' Perspectives on AI- and Human-Composed Progressive Metal Music](https://mlsanity.com/paper/2407.21615/) (2024)
- [MambaFoley: Foley Sound Generation using Selective State-Space Models](https://mlsanity.com/paper/2409.09162/) (2024)
- [Mitigating data replication in text-to-audio generative diffusion models through anti-memorization guidance](https://mlsanity.com/paper/2509.14934/) (2025)
- [The Interpretation Gap in Text-to-Music Generation Models](https://mlsanity.com/paper/2407.10328/) (2024)
- [Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classification](https://mlsanity.com/paper/2504.03329/) (2025)

## Built on by (most cited first, 1 of 1)

- [MusGU+: Toward a Musician-Centered Evaluation Framework and Discovery Tool for Generative Music AI](https://mlsanity.com/paper/2608.30940/) (2026)

## 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.