Ressources.ircam

Abstract: Interactive Improvisation Systems involve at least three cooperating and concurrent expert agents: machine listening, machine learning, model based generation. Machine listening may occur during the initial learning stage (off-line or real-time in live situations) and during the generation stage as well in order to align the computer production with current live input. Machine learning can be based on any statistical model capturing significant signal or symbolic stream of features that can be exploited in the generation stage. In particular, the OMax interactive computational improvisation environment will be presented.

Bio: Gerard Assayag is head of the Music Representation Research Group at IRCAM (Institut de Recherche et de Coordination Acoustique/Musique) in Paris, and head of the STMS (Sciences and Technologies of Music and Sound) Ircam/CNRS Lab. Born in 1960, he studied computer science, music and linguistics. In 1980, while still a student, he won research awards in "Art and the Computer", a national software contest launched in 1980 by the French Ministry of Research, and another one in the "Concours Micro", a contest in computing in the arts using early micro-computers. In the mid-eighties, he wrote the first IRCAM environment for score-oriented Computer Assisted Composition. In the mid-nineties he created, with Carlos Agon, the OpenMusic environment which is currently the standard for computational composition and musicology. . The concept behind OpenMusic is to provide a visual counterpart of major programming paradigms (such as functional, object and logical programming) along with an extensive set of musical classes and methods, plus an original metaphor for representing musical time in its logical, as well as chronological, aspects. Recently Gerard Assayag has created with other colleagues the OMax computaitonal improvisation system based on machine listening and machine learning and has become a widely recognized reference in the field. Gerard Assayag's research interests center on music representation issues, and include computer language paradigms, machine learning, constraint and visual programming, computational musicology, music modeling, and computer-assisted composition. His research results are regularly published in proceedings, books and journals.