Treffer: Modelling expressive performance : A regression tree approach based on strongly typed genetic programming

Title:
Modelling expressive performance : A regression tree approach based on strongly typed genetic programming
Authors:
HAZAN, Amaury, RAMIREZ, Rafael, MAESTRE, Esteban, PEREZ, Alfonso, PERTUSA, Antonio
Source:
Applications of evolutionary computing (EvoWorkshops 2006)Lecture notes in computer science. :676-687
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 13 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Bioinformatics, Bioinformatique, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Intelligence artificielle, Artificial intelligence, Acoustique musicale, Musical acoustics, Acústica musical, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Durée, Duration, Duración, Modèle donnée, Data models, Modèle régression, Regression model, Modelo regresión, Modélisation, Modeling, Modelización, Musique, Music, Música, Mélodie, Melody, Melodía, Méthode arborescente, Tree structured method, Método arborescente, Structure arborescente, Tree structure, Estructura arborescente
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Music Technology Group, Pompeu Fabra University, Ocata 1, Barcelona 08003, Spain
Departamento de Lenguajes y Sistemas Informàticos, Universidad de Alicante, Alicante, Spain
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19131302
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19131302
Database:
PASCAL Archive

Weitere Informationen

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 [I], 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.