Novice Users' Evaluation of Two Multi-track Music Machines for AI-Assisted Music Composition: Usability, User Experience and Acceptance

Renaud Bougueng Tchemeube; Jeff Ens; Keon Ju Maverick Lee; Philippe Pasquier; Jean-Baptiste Rolland; Yvan Grabit; Maryam Safi

Novice Users' Evaluation of Two Multi-track Music Machines for AI-Assisted Music Composition: Usability, User Experience and Acceptance
Image credit: Renaud Bougueng Tchemeube; Jeff Ens; Keon Ju Maverick Lee; Philippe Pasquier; Jean-Baptiste Rolland; Yvan Grabit; Maryam Safi

Abstract:

The maturity of creative AI systems in the arts raises new questions regarding their integration into creative practices. The music field is no exception, and is seeing a rise in new creative AI tools, notably in music composition. We study how these systems and their design impact user’s adoption. Specifically, we conducted a user study with 98 novice participants evaluating usability, user experience, and technology acceptance for two computer-assisted composition (CAC) systems: MMM-Cubase v2 and Calliope. Findings show both systems are easy to control and use, with Calliope being easier to use and more immersive. 76.9% (MMM-Cubase v2) and 72.9% (Calliope) of users report positive predicted future use, while the novel and efficient workflow contributes to lower barriers to music-making. Depth of control and model transparency remain outstanding issues while users highlight concerns over loss of musical diversity and skill learning.