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.
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References
Ramirez, R., Hazan, A.: Understanding expressive music performance using genetic algorithms. In: Third European Workshop on Evolutionary Music and Art, Lauzane, Switzerland (2005)
Ramirez, R., Hazan, A., Maestre, E.: Intra-note features prediction model for jazz saxophone performance. In: International Computer Music Conference, Barcelona, Spain (2005)
Ramirez, R., Hazan, A.: A tool for generating and explaining expressive music performances of monophonic jazz melodies. International Journal on Artificial Intelligence Tools (2006) (to appear)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Quinlan, J.R.: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, pp. 343–348 (1992)
Murthy, S.: Automatic construction of decision trees from data: A multidisciplinary survey. Data Mining and Knowledge Discovery 2, 345–389 (1998)
Koza, J.: Genetic Programming: On the programming of Computers by means of natural Selection. MIT Press, Cambridge (1992)
Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbour (1975)
Montana, D.: Strongly typed genetic programming. Technical Report #7866, 10 Moulton Street, Cambridge, MA 02138, USA (1993)
Gómez, E., Grachten, M., Amatriain, X., Arcos, J.: Melodic characterization of monophonic recordings for expressive tempo transformations. In: Proceedings of Stockholm Music Acoustics Conference 2003, Stockholm, Sweden (2003)
Gagné, C., Parizeau, M.: Open beagle manual. technical report rt-lvsn-2003-01- v300-r1. Technical report, Laboratoire de Vision et Syst‘emes numérique, Université de Laval (2005)
Grachten, M., Arcos, J., López de Mántaras, R.: Melody retrieval using the implication/realization model. In: Online Proceedings of the 6th International Conference on Music Information Retrieval, London, UK (2005)
Conklin, D., Anagnostopoulou, C.: Representation and discovery of multiple viewpoint patterns. In: Proceedings of the International Computer Music Conference, La Havana, Cuba, pp. 479–485 (2001)
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© 2006 Springer-Verlag Berlin Heidelberg
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Hazan, A., Ramirez, R., Maestre, E., Perez, A., Pertusa, A. (2006). Modelling Expressive Performance: A Regression Tree Approach Based on Strongly Typed Genetic Programming. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_64
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DOI: https://doi.org/10.1007/11732242_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33237-4
Online ISBN: 978-3-540-33238-1
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