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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{hazan:evows06,

• 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",
• 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

Citations