OMax brothers: a dynamic yopology of agents for improvization learning

Authors:
Gérard Assayag;Georges Bloch;Marc Chemillier;Arshia Cont;Shlomo Dubnov
Affiliations:
IRCAM-CNRS UMR Stms, Paris, France;University of Strasbourg, Strasbourg France;University of Caen, Caen Cedex France;IRCAM-UCSD, Paris, France;UCSD, La Jolla, CA
Venue:
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
Year:
2006

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Abstract

We describe a multi-agent architecture for an improvization oriented musician-machine interaction system that learns in real time from human performers. The improvization kernel is based on sequence modeling and statistical learning. The working system involves a hybrid architecture using two popular composition/perfomance environments, Max and OpenMusic, that are put to work and communicate together, each one handling the process at a different time/memory scale. The system is capable of processing real-time audio/video as well as MIDI. After discussing the general cognitive background of improvization practices, the statistical modeling tools and the concurrent agent architecture are presented. Finally, a prospective Reinforcement Learning scheme for enhancing the system's realism is described.