Gérard Assayag

Learn More
We describe variable markov models we have used for statistical learning of musical sequences, then we present the factor oracle, a data structure proposed by Crochemore & al for string matching. We show the relation between this structure and the previous models and indicate how it can be adapted for learning musical sequences and generating improvisations(More)
T he ability to construct a musical theory from examples presents a great intellectual challenge that, if successfully met, could foster a range of new creative applications. Inspired by this challenge, we sought to apply machine-learning methods to the problem of musical style modeling. Our work so far has produced examples of musical generation and(More)
This paper presents the computer-assisted composition environment OpenMusic and introduces OM 5.0, a new cross-platform release. The characteristics of this system will be exposed, with examples of applications in music composition and analysis. Resumo. Este artigo apresenta o ambiente de composição assistida por computador OpenMusic, e introduz OM 5.0, uma(More)
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,(More)
In this paper, we describe and compare two methods for unsupervised learning of musical style, both of which perform analyses of musical sequences and then compute a model from which new interpretations / improvisations close to the original's style can be generated. In both cases, an important part of the musical structure is captured, including rhythm,(More)
Machine improvisation and related style simulation problems usually consider building representations of time-based media data, such as music, either by explicit coding of rules or applying machine learning methods. Stylistic learning applies such methods to musical sequences in order to capture salient musical features and organize these features into a(More)