Designing Deep Reinforcement Learning for Human Parameter Exploration

Abstract

Software tools for generating digital sound often present users with high-dimensional, parametric interfaces, that may not facilitate exploration of diverse sound designs. In this paper, we propose to investigate artificial agents using deep reinforcement learning to explore parameter spaces in partnership with users for sound design. We describe a series of user-centred studies to probe the creative benefits of these agents and adapting their design to exploration. Preliminary studies observing users' exploration strategies with parametric interfaces and testing different agent exploration behaviours led to the design of a fully-functioning prototype, called Co-Explorer, that we evaluated in a workshop with professional sound designers. We found that the Co-Explorer enables a novel creative workflow centred on human-machine partnership, which has been positively received by practitioners. We also highlight varied user exploration behaviors throughout partnering with our system. Finally, we frame design guidelines for enabling such co-exploration workflow in creative digital applications. KeywordsInteraction Design • Machine Learning • Audio/Video Preprint. Under review for publication in ACM Transactions on Computer-Human Interaction (TOCHI).

Artificial Intelligence in Music and Performance: A Subjective Art-Research Inquiry

“…However, most of these attempts have highlighted the inherent challenges of this approach due to the difficulty of grasping what AI can and cannot do [56]. In my work (first author), I am exploring how the computational learning mechanisms themselves can become interactive in order to foster exploration and human learning, as we recently explored in the specific domain of sound design [50].…”