Abstract
We use stochastic deterministic finite automata to model musical styles: a same automaton can be used to classify new melodies but also to generate them. Through grammatical inference these automata are learned and new pieces of music can be parsed. We show that this works by proposing promising classification results.
This work was supported in part by the IST Programme of the European Community, under the Pascal Network of Excellence, IST-2002-506778. This publication only reflects the authors’ views.
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Narmour, E.: The analysis and cognition of melodic complexity: The implication-realization model. University of Chicago Press, Chicago (1992)
Cruz, P., Vidal, E.: Learning regular grammars to model musical style: Comparing different coding schemes. In: Honavar, V.G., Slutzki, G. (eds.) ICGI 1998. LNCS (LNAI), vol. 1433, pp. 211–222. Springer, Heidelberg (1998)
Sakakibara, Y.: Recent advances of grammatical inference. Theoretical Computer Science 185, 15–45 (1997)
de la Higuera, C.: A bibliographical study of grammatical inference. Pattern Recognition (to appear, 2005)
Thollard, F., Dupont, P., de la Higuera, C.: Probabilistic DFA inference using Kullback-Leibler divergence and minimality. In: Proc. 17th International Conf. on Machine Learning, pp. 975–982. Morgan Kaufmann, San Francisco (2000)
Kermorvant, C., de la Higuera, C.: Learning languages with help. In: Adriaans, P., Fernau, H., van Zaanen, M. (eds.) ICGI 2002. LNCS (LNAI), vol. 2484, pp. 161–173. Springer, Heidelberg (2002)
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de la Higuera, C., Piat, F., Tantini, F. (2006). Learning Stochastic Finite Automata for Musical Style Recognition. In: Farré, J., Litovsky, I., Schmitz, S. (eds) Implementation and Application of Automata. CIAA 2005. Lecture Notes in Computer Science, vol 3845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11605157_31
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DOI: https://doi.org/10.1007/11605157_31
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