mlsanityData to 7 Oct 2026

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

arXiv 2304.03407 ↗First posted 6 Apr 2023cs.HC cs.AI cs.SDcited by 4

Not on the map. Why?

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 67 references that are in the map. First posted on arXiv 6 Apr 2023.

Builds on

10 of 67 references.

References · 10

  1. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  2. 2023MusicLM: Generating Music From Textcited by 442
  3. 2021Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AIcited by 38
  4. 2020Automatic multitrack mixing with a differentiable mixing console of neural audio effectscited by 27
  5. 2022Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effectscited by 18
  6. 2021Efficient neural networks for real-time modeling of analog dynamic range compressioncited by 15
  7. 2021Differentiable Artificial Reverberationcited by 11
  8. 2022Automatic music mixing with deep learning and out-of-domain datacited by 11
  9. 2021Direct design of biquad filter cascades with deep learning by sampling random polynomialscited by 8
  10. 2019A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressorcited by 7

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2024Music Foundation Model as Generic Booster for Music Downstream Taskscited by 6
  2. 2023The Role of Communication and Reference Songs in the Mixing Process: Insights from Professional Mix Engineerscited by 1
  3. 2024Composers' Evaluations of an AI Music Tool: Insights for Human-Centred Designcited by 0
  4. 2025MixAssist: An Audio-Language Dataset for Co-Creative AI Assistance in Music Mixingcited by 0