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

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Amaury Hazan and Rafael Ramirez and 
                 Esteban Maestre and Alfonso Perez and Antonio Pertusa",
  title =        "Modelling Expressive Performance: a Regression Tree
                 Approach Based on Strongly Typed Genetic Programming",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoWorkshops2006: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
                 {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}",
  year =         "2006",
  month =        "10-12 " # apr,
  editor =       "Franz Rothlauf and Jurgen Branke and 
                 Stefano Cagnoni and Ernesto Costa and Carlos Cotta and 
                 Rolf Drechsler and Evelyne Lutton and Penousal Machado and 
                 Jason H. Moore and Juan Romero and George D. Smith and 
                 Giovanni Squillero and Hideyuki Takagi",
  series =       "LNCS",
  volume =       "3907",
  publisher =    "Springer Verlag",
  address =      "Budapest",
  publisher_address = "Berlin",
  keywords =     "genetic algorithms, genetic programming, STGP",
  ISBN =         "3-540-33237-5",
  pages =        "676--687",
  URL =          "",
  DOI =          "doi:10.1007/11732242_64",
  abstract =     "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.",
  notes =        "part of \cite{evows06}",

Genetic Programming entries for Amaury Hazan Rafael Ramirez Esteban Maestre Alfonso Perez Antonio Pertusa