# An investigation of AI integration in sound designer workflows and experiences

arXiv: https://arxiv.org/abs/2605.27174 · HTML: https://mlsanity.com/paper/2605.27174/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 6 of 6)

- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [Music Transformer](https://mlsanity.com/paper/1809.04281/) (2018)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [Generative AI in the Wild: Prospects, Challenges, and Strategies](https://mlsanity.com/paper/2404.04101/) (2024)
- [The Machine Learning Algorithm as Creative Musical Tool](https://mlsanity.com/paper/1611.00379/) (2016)
- [A Demand-Driven Perspective on Generative Audio AI](https://mlsanity.com/paper/2307.04292/) (2023)

## Abstract (arXiv)

Artificial intelligence is increasingly being integrated into professional audio production workflows, yet a gap persists between the tools developers produce and the requirements of practising sound designers. This paper investigates this gap through a mixed-methods study comprising a survey of 76 practitioners and follow-up semi-structured interviews with 20 industry professionals. Results were analysed using descriptive statistical analysis and thematic analysis to identify patterns across both datasets. Five themes emerged from our analysis: Context, Workflow, Potential, Risks, and Right Use. Our work indicates that current AI tools perform adequately in fast-consumption media contexts but lack the narrative sophistication required for high-end sound design (films, immersive experiences etc). Practitioners demonstrate a preference for assistive, task-specific applications, particularly in audio restoration and library management, over end-to-end generative systems. This work contributes to the on-going discussion on the use of AI and AI-enhanced tools in the creative industries. We report on the current status of the field from the point of view of sound designers and creative audio practitioners, and offer a set of recommendation for sound technologist and developers based on our findings to guide the development of more informed AI tools for sound design.