Exploring Reinforcement Learning For Mobile Percussive Collaboration
Nate Derbinsky, Georg Essl · 2012
This paper presents a system for mobile percussive collaboration. We show that reinforcement learning can incrementally learn percussive beat patterns played by humans and supports realtime collaborative performance in the absence of one or more performers. This work leverages an existing integration between urMus and Soar and addresses multiple challenges involved in the deployment of machine-learning algorithms for mobile music expression, including tradeoffs between learning speed & quality; interface design for human collaborators; and real-time performance and improvisation.