Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
C4.5: programs for machine learning
C4.5: programs for machine learning
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
A case based approach to expressivity-aware tempo transformation
Machine Learning
Inducing a generative expressive performance model using a sequential-covering genetic algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A genetic rule-based model of expressive performance for jazz saxophone
Computer Music Journal
Tempoexpress: an expressivity-preserving musical tempo transformation system
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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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.