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Exploring Reinforcement Learning for Mobile Percussive Collaboration

Nate Derbinsky, and Georg Essl

Proceedings of the International Conference on New Interfaces for Musical Expression

Abstract

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.

Citation

Nate Derbinsky, and Georg Essl. 2012. Exploring Reinforcement Learning for Mobile Percussive Collaboration. Proceedings of the International Conference on New Interfaces for Musical Expression. DOI: 10.5281/zenodo.1178243 [PDF]

BibTeX Entry


@inproceedings{nime2012_Derbinsky,
author = {Nate Derbinsky and Georg Essl},
title = {Exploring Reinforcement Learning for Mobile Percussive Collaboration},
pages = {70--75},
booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression},
year = {2012},
publisher = {University of Michigan},
address = {Ann Arbor, Michigan},
issn = {2220-4806},
doi = {10.5281/zenodo.1178243},
url = {https://nime.org/proceedings/2012/nime2012_241.pdf},
keywords = {Mobile music, machine learning, cognitive architecture},
abstract = {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.}
}