Intelligent Real-Time Music Accompaniment for Constraint-Free Improvisation

Abstract

Computational Intelligence encompasses tools that allow the fast convergence and adaptation to several problems, a fact that makes them eligible for real-time implementations. The paper at hand discusses the utilization of intelligent algorithms (i.e. Differential Evolution and Genetic Algorithms) for the creation of an adaptive system that is able to provide real-time automatic music accompaniment to a human improviser. The main goal of the presented system is to generate accompanying music based on the local human musician's tonal, rhythmic and intensity playing style, incorporating no prior knowledge about the improvisers intentions. Compared to existing systems previously proposed, this work introduces a constraint-free improvisation environment where the most important musical characteristics are automatically adapted to the human performer's playing style, without any prior information. This fact allows the improviser to have maximal control over the tonal, rhythmic and intensity improvisation directions.