# Adoption of AI Technology in the Music Mixing Workflow: An Investigation

arXiv: https://arxiv.org/abs/2304.03407 · HTML: https://mlsanity.com/paper/2304.03407/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 67 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI](https://mlsanity.com/paper/2105.05424/) (2021)
- [Automatic multitrack mixing with a differentiable mixing console of neural audio effects](https://mlsanity.com/paper/2010.10291/) (2020)
- [Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects](https://mlsanity.com/paper/2211.02247/) (2022)
- [Efficient neural networks for real-time modeling of analog dynamic range compression](https://mlsanity.com/paper/2102.06200/) (2021)
- [Differentiable Artificial Reverberation](https://mlsanity.com/paper/2105.13940/) (2021)
- [Automatic music mixing with deep learning and out-of-domain data](https://mlsanity.com/paper/2208.11428/) (2022)
- [Direct design of biquad filter cascades with deep learning by sampling random polynomials](https://mlsanity.com/paper/2110.03691/) (2021)
- [A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressor](https://mlsanity.com/paper/1905.01022/) (2019)

## Built on by (most cited first, 4 of 4)

- [Music Foundation Model as Generic Booster for Music Downstream Tasks](https://mlsanity.com/paper/2411.01135/) (2024)
- [The Role of Communication and Reference Songs in the Mixing Process: Insights from Professional Mix Engineers](https://mlsanity.com/paper/2309.03404/) (2023)
- [Composers' Evaluations of an AI Music Tool: Insights for Human-Centred Design](https://mlsanity.com/paper/2412.10968/) (2024)
- [MixAssist: An Audio-Language Dataset for Co-Creative AI Assistance in Music Mixing](https://mlsanity.com/paper/2507.06329/) (2025)

## Abstract (arXiv)

The integration of artificial intelligence (AI) technology in the music industry is driving a significant change in the way music is being composed, produced and mixed. This study investigates the current state of AI in the mixing workflows and its adoption by different user groups. Through semi-structured interviews, a questionnaire-based study, and analyzing web forums, the study confirms three user groups comprising amateurs, pro-ams, and professionals. Our findings show that while AI mixing tools can simplify the process and provide decent results for amateurs, pro-ams seek precise control and customization options, while professionals desire control and customization options in addition to assistive and collaborative technologies. The study provides strategies for designing effective AI mixing tools for different user groups and outlines future directions.