Machine Improvisation

Machine Improvisation uses computer algorithms to create improvisation on existing music materials. This is usually done by sophisticated recombination of musical phrases extracted from existing music, either live or pre-recorded. In order to achieve credible improvisation in particular style, machine improvisation uses machine learning and pattern matching algorithms to analyze existing musical examples. The resulting patterns are then used to create new variations "in the style" of the original music, developing a notion of stylistic reinjection. This is different from other improvisation methods with computers that use algorithmic composition to generate new music without performing analysis of existing music examples.

Statistical style modeling

Style modeling implies building a computational representation of the musical surface that captures important stylistic features from data. Statistical approaches are used to capture the redundancies in terms of pattern dictionaries or repetitions, which are later recombined to generate new musical data. Style mixing can be realized by analysis of a database containing multiple musical examples in different styles. Machine Improvisation builds upon a long musical tradition of statistical modeling that began with Hiller and Isaacson's Illiac Suite in the 1950s and Xenakis' uses of Markov chains and stochastic processes. Modern methods include the use of lossless data compression for incremental parsing, Prediction Suffix Tree and string searching by factor oracle algorithm

Uses of Machine Improvisation

Machine Improvisation encourages musical creativity by providing automatic modeling and transformation structures for existing music. This creates a natural interface with the musician without need for coding musical algorithms. In live performance, the system re-injects the musician's material in several different ways, allowing a semantics-level representation of the session and a smart recombination and transformation of this material in real-time. In offline version, Machine Improvisation can be used to achieve style mixing, an approach inspired by Vannevar Bush's memex imaginary machine.

Implementations

Matlab implementation of the Factor Oracle machine improvisation can be found as part of Computer Audition toolbox.
OMax is a software environment developed in IRCAM. OMax uses OpenMusic and Max. It is based on researches on stylistic modeling carried out by Gerard Assayag and Shlomo Dubnov and on researches on improvisation with the computer by G. Assayag, M. Chemillier and G. Bloch (Aka the OMax Brothers) in the Ircam Music Representations group.

Musicians working with machine improvisation

Gerard Assayag (IRCAM, France), Tim Blackwell (Goldsmiths College, Great Britain), George Bloch (Composer, France), Marc Chemiller (IRCAM/CNRS, France), Nick Collins (University of Sussex, UK) Shlomo Dubnov (Composer, Israel / USA), Mari Kimura (Juilliard, New York City), George Lewis (Columbia University, New York City), Bernard Lubat (Pianist, France), Joel Ryan (Institute of Sonology, Netherlands), Michel Waisvisz (STEIM, Netherlands), David Wessel (CNMAT, California), Michael Young (Goldsmiths College, Great Britain), Pietro Grossi (CNUCE, Institute of the National Research Council, Pisa, Italy)