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

TL;DR

This paper examines how AI technology is adopted in music mixing workflows across different user groups, revealing varying needs and preferences, and suggesting strategies for designing effective AI tools tailored to each group.

Contribution

It provides an empirical analysis of AI adoption in music mixing, identifying user group-specific requirements and proposing design strategies for AI tools in this domain.

Findings

AI tools simplify mixing for amateurs

Pro-ams seek customization and control

Professionals want assistive and collaborative features

Abstract

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.