smArt: AI for Creative Applications in the Flemish Music Sector

Artificial intelligence is rapidly permeating through every link in the music and audio chain, from songwriting and sound design to mixing, mastering, and live experiences. Yet for the Flemish music sector - and internationally - there is currently no independent, practice-oriented overview of which AI applications are relevant, how they fit into existing workflows, what their societal and economic consequences are, and how human added value can remain central.

In "smArt", a multidisciplinary team from PXL-Music Research investigates the role of AI in the Flemish music sector. The project combines a landscape analysis of AI tools and applications with an in-depth study of the workflows, motivations, and concerns of (pre-)professional practitioners. Key questions include: which tasks are repetitive and suited to automation, which tasks give energy and professional identity, and how does AI affect autonomy, competence, and relatedness - the three basic needs from Self-Determination Theory (Ryan & Deci, 2000) - and how does it shift the balance between demands and resources, as described in the Job Demands-Resources (JD-R) model (Bakker & Demerouti, 2007)? On this basis, we evaluate existing types of tools and map out workable pipelines that practitioners in Flanders can realistically adopt.

Over three years, smArt develops a living, field-validated taxonomy of AI applications, a Flemish AI map per subdomain (composition, production, live, …), and concrete guidelines for practitioners, organisations, education, and policy. We deliberately start with a sharply defined profile - the music producer - and expand from there to other roles in the chain. Results will be integrated into PXL-Music degree programmes, shared with sector and education networks, and used as substantive contributions to international events and collaborations in this field.