Modelling Expressive Performance: A Regression Tree Approach Based on Strongly Typed Genetic Programming

  • Amaury Hazan
  • Rafael Ramirez
  • Esteban Maestre
  • Alfonso Perez
  • Antonio Pertusa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)

Abstract

This paper presents a novel Strongly-Typed Genetic Programming approach for building Regression Trees in order to model expressive music performance. The approach consists of inducing a Regression Tree model from training data (monophonic recordings of Jazz standards) for transforming an inexpressive melody into an expressive one. The work presented in this paper is an extension of [1], where we induced general expressive performance rules explaining part of the training examples. Here, the emphasis is on inducing a generative model (i.e. a model capable of generating expressive performances) which covers all the training examples. We present our evolutionary approach for a one-dimensional regression task: the performed note duration ratio prediction. We then show the encouraging results of experiments with Jazz musical material, and sketch the milestones which will enable the system to generate expressive music performance in a broader sense.

Keywords

Tempo Acoustics Extractor 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Amaury Hazan
    • 1
  • Rafael Ramirez
    • 1
  • Esteban Maestre
    • 1
  • Alfonso Perez
    • 1
  • Antonio Pertusa
    • 2
  1. 1.Music Technology GroupPompeu Fabra UniversityBarcelonaSpain
  2. 2.Departamento de Lenguajes y Sistemas InformáticosUniversidad de AlicanteAlicanteSpain

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