Abstract
An application of Grammatical Inference (GI) in the field of Music Processing is presented, were Regular Grammars are used for modeling musical style. The interest in modeling musical style resides in the use of these models in applications, such as Automatic Composition and Automatic Musical Style Recognition. We have studied three GI Algorithms, which have been previously applied successfully in other fields. In this work, these algorithms have been used to learn a stochastic grammar for each of three different musical styles from examples of melodies. Then, each of the learned grammars was used to stochastically synthesize new melodies (Composition) or to classify test melodies (Style Recognition). Our previous studies in this field showed the need of a proper music coding scheme. Different coding schemes are presented and compared according to results in Composition and Style Recognition. Results from previous studies have been improved.
This work has been partially supported by European Union ESPRIT LTR Project 30268 “EUTRANS”.
Preview
Unable to display preview. Download preview PDF.
References
Amengual J.C., Vidal E. and Benedí J.M. October 1996. Simplifying Language through Error-Correcting Decoding. Proceedings of the ICSLP96 (IV International Conference on Spoken Language Processing), pp. 841–844, Philadelphia, PA., USA, 3–6.
Amengual J.C. and Vidal E. 1996. Two Different Approaches for Cost-efficient Viterbi Parsing with Error Correction. Advances in Structural and Syntactic Pattern Recognition, pp. 30–39. P. Perner, P. Wang and A. Rosenfeld (eds.). LNCS 1121. Springer-Verlag.
Carrasco, R.C.; Oncina, J. 1994. Learning Stochastic Regular Grammars by means of a State Merging Method. “Grammatical Inference and Applications”. Carrasco, R.C.;Oncina, J. eds. Springer-Verlag, (Lecture notes in Artificial Intelligence (862)).
Cruz P.P. 1996. Estudio de diversos algoritmos de Inferencia Gramatical para el Reconocimiento Automático de Estilos Musicales y la Composición de melodías en dichos estilos. PFC. Facultad de Informática. Universidad Politécnica de Valencia.
Cruz P.P. 1997. A study of Grammatical Inference Algorithms in Automatic Music Composition. Proceedings of the SNRFAI97 (VII Simposium Nacional de Reconocimiento de Formas y Análisis de Imágenes). Vol. 1, pp. 43–48. Sanfeliu A.,Villanueva J.J. and Vitrià J. Eds. Centre de Visió per Computador, Universidad Autónomade Barcelona.
Cruz P.P. 1997. A study of Grammatical Inference Algorithms in Automatic Music Composition and Musical Style Recognition. Proceedings from the ‘Workshop on Automata Induction, Grammatical Inference, and Language Acquisition', celebrated during the ICML97 (The Fourteenth International Conference on Machine Learning) Nashville, Tennessee. Electronic publication in the Workshop Web page (http://www.cs.cmu.edu/-pdupont/mlworkshop.html).
Eberhart, R.C.; Dobbins, R.W. 1990. Neural Network PC Tools. Academic Press Inc, pp.295–312.
Forney, G. D. 1973. The Viterbi algorithm. IEEE Proc. 3, pp. 268–278.
Fu, K.S. 1982. Syntactic Pattern Recognition and Applications. Prentice Hall.
García P., Vidal E., Casacuberta F. 1987. Local Languages, the Successor Method, and a step towards a general methodology for the Inference of Regular Grammars. IEEE Trans.on Pattern Analysis and Machine Intelligence. Vol.PAMI-9, No.6, pp.841–844.
García, P.; Vidal, E. 1990. Inference of K-Testable Languages In the Strict Sense and Application to Syntactic Pattern Recognition. IEEE Trans. on PAMI, 12, 9, pp. 920–925.
Nuñez, A. 1992. Informática y Electrónica Musical. Ed. Paraninfo.
Rulot, H.; Vidal, E. 1987. Modelling (sub)string-length based constraints through a Gramatical Inference method. NATO ASI Series, Vol. F30 Pattern Recognition Theory and Applications, pp. 451–459. Springer-Verlag.
Rumsey, F. 1994. MIDI Systems & Control. Ed. Focal Press.
Schwanauer S.M.; Levitt D.A. 1993. Machine Models of Music. The MIT Press.
Segarra, E. 1993. Una Aproximación Inductiva a la Comprensión del Discurso Continuo. Facultad de Informática. Universidad Politécnica de Valencia.
Todd P. 1989. A sequential network design for musical applications. Proc. of the 1988 Connectionist Models Summer School. Morgan Kaufmann Publishers, pp. 76–84.
Vidal E., Casacuberta F., García P. 1995. Grammatical Inference and Automatic Speech Recognition. In “Speech Recognition and Coding: New Advances and Trends”, A.Rubio y J.M.López, Eds., Springer Verlag.
Vidal E., Llorens D. 1996. Using knowledge to improve N-Gram Language Modelling through the MGGI methodology. In ‘Grammatical Inference: Learning Syntax from Sentences'. Proc. of 3rd ICGI. L.Miclet, C. de la Higuera (Eds.). Springer-Verlag (Lect.Notes in Artificial Intelligence, Vol.1147).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cruz-Alcázar, P.P., Vidal-Ruiz, E. (1998). Learning regular grammars to model musical style: Comparing different coding schemes. In: Honavar, V., Slutzki, G. (eds) Grammatical Inference. ICGI 1998. Lecture Notes in Computer Science, vol 1433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0054077
Download citation
DOI: https://doi.org/10.1007/BFb0054077
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64776-8
Online ISBN: 978-3-540-68707-8
eBook Packages: Springer Book Archive