OMax brothers: A dynamic yopology of agents for improvization learning

Gérard Assayag, Georges Bloch, Marc Chemillier, Arshia Cont, Shlomo Dubnov

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

74 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings of the 1st ACM Workshop on Audio and Music Computing for Multimedia, AMCMM'06
Pages125-132
Number of pages8
DOIs
StatePublished - 1 Dec 2006
Externally publishedYes
Event1st ACM Workshop on Audio and Music Computing for Multimedia, AMCMM'06, co-located with the 2006 ACM International Multimedia Conference - Santa Barbara, CA, United States
Duration: 27 Oct 200727 Oct 2007

Publication series

NameProceedings of the ACM International Multimedia Conference and Exhibition

Conference

Conference1st ACM Workshop on Audio and Music Computing for Multimedia, AMCMM'06, co-located with the 2006 ACM International Multimedia Conference
Country/TerritoryUnited States
CitySanta Barbara, CA
Period27/10/0727/10/07

Keywords

  • Computer music
  • Concurrent agents
  • Improvization
  • Machine learning
  • Man machine interaction
  • Sequence modeling
  • Statistical learning
  • Style modeling
  • Variable memory markov systems

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