A Genetic Rule-Based Model of Expressive Performance for Jazz Saxophone
- A Genetic Rule-Based Model of Expressive Performance for Jazz Saxophone
Evolutionary computation (De Jong et al. 1993) is being considered with growing interest in musical applications. One of the music domains in which evolutionary computation has made the most impact is music composition. A number of evolutionary systems for composing musical material have been proposed (e.g., Horner and Goldberg 1991; Dahlstedt and Nordhal 2001). In addition to music composition, evolutionary computing has been considered in music improvisation applications where an evolutionary algorithm typically models a musician's improvising (e.g., Biles 1994). Nevertheless, little research focusing on the use of evolutionary computation for expressive-performance analysis has been reported.
Traditionally, expressive performance has been studied using empirical approaches based on statistical analysis (e.g., Repp 1992), mathematical modeling (e.g., Todd 1992), and analysis-by-synthesis (e.g., Friberg et al. 1998). In these approaches, humans are responsible for devising a theory or a mathematical model that captures different aspects of musical expressive performance. The theory or model is later tested on real performance data to determine its accuracy.
In this article, we describe an approach to investigating musical expressive performance based on evolutionary computation. Instead of manually modeling expressive performance and testing the model on real musical data, we let a computer execute a sequential-covering genetic algorithm to automatically discover regularities and performance principles from real performance data, consisting of audio recordings of jazz standards. The algorithm combines sequential covering (Michalski 1969) and genetic algorithms (Holland 1975). The sequential-covering component of the algorithm incrementally constructs a set of rules by learning new rules one at a time, removing the positive examples covered by the latest rule before attempting to learn the next rule. The genetic component of the algorithm learns each of the new rules by applying a genetic algorithm.
The algorithm provides an interpretable specifi cation of the expressive principles applied to an interpretation of piece of music and, at the same time, it provides a generative model of expressive performance, namely, a model capable of generating a computer-music performance with the timing and energy expressiveness that characterizes human-generated music.
The use of evolutionary techniques for modeling expressive music performance provides a number of potential advantages over other supervised-learning algorithms. By applying our evolutionary algorithm, it is possible to explore and analyze the induced expressive model as it "evolves," to guide and interact with the evolution of the model, and to obtain different models resulting from different executions of the algorithm. This last point is very relevant to the task of modeling expressive music performance, because it is desirable to obtain a non-deterministic model capturing the different possible interpretations a performer may produce for a given piece.
The rest of this article is organized as follows. First, we report on related work and describe how we extract a set of acoustic features from the audio recordings. We then describe our evolutionary approach for inducing an expressive music-performance computational model. Finally, we present some conclusions and indicate some areas of future research.
Related Work
Evolutionary computation has been considered with growing interest in musical applications (Miranda 2004). A large number of experimental [End Page 38] systems using evolutionary techniques to generate musical compositions have been proposed, including Cellular Automata Music (Millen 1990), a Cellular Automata Music Workstation (Hunt, Kirk, and Orton 1991), CAMUS (Miranda 1993), MOE (Degazio 1999), GenDash (Waschka 1999), CAMUS 3D (McAlpine, Miranda, and Hogar 1999), Vox Populi (Manzolli et al. 1999), Synthetic Harmonies (Bilotta, Pantano, and Talarico 2000), Living Melodies (Dahlstedt and Nordhal 2001), and Genophone (Mandelis 2001). Composition systems based on genetic algorithms generally follow the standard genetic-algorithm approach for evolving musical materials such as melodies, rhythms, and chords. As a result, such compositional systems share the core approach with the one presented in this article. For example, Vox Populi (Manzolli et al. 1999) evolves populations of chords of four notes, each of which is represented as a seven-bit string. The genotype of a chord therefore consists of a string of 28 bits, and the genetic operations of crossover and mutation are applied to these...
