Gérard Assayag
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.
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Digital Library
- Assayag, G., Dubnov, S., Using Factor Oracles for Machine Improvization, G. Assayag, V. Cafagna, M. Chemillier (eds.), Formal Systems and Music special issue, Soft Computing 8, pp. 1432--7643, September 2004. Google ScholarDigital Library
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Index Terms
- OMax brothers: a dynamic yopology of agents for improvization learning
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