Abstract
A Musical Style Identification model based on Grammatical Inference (GI) is presented. Under this model, regular grammars are used for modeling Musical Style. Style Classification can be used to implement or improve content based retrieval in multimedia databases, musicology or music education. In this work, several GI Techniques are used to learn, from examples of melodies, a stochastic grammar for each of three different musical styles. Then, each of the learned grammars provides a confidence value of a composition belonging to that grammar, which can be used to classify test melodies. A very important issue in this case is the use of a proper music coding scheme, so different coding schemes are presented and compared, achieving a 3% classification error rate.
This work has been partially supported by CYCIT Project TIC2000-1703-C03-01 “TAR”.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Amengual, J.C., Vidal, E.: Two Different Approaches for Cost-efficient Viterbi Parsing with Error Correction. In: Perner, P., Rosenfeld, A., Wang, P. (eds.) SSPR 1996. LNCS, vol. 1121, pp. 30–39. Springer, Heidelberg (1996)
Cambouropoulos, E., et al.: Pattern Processing in Melodic Sequences. Computers and the Humanities 35(1), 9–21 (2001)
Carrasco, R.C., Oncina, J.: Learning Stochastic Regular Grammars by means of a State Merging Method. In: Carrasco, R.C., Oncina, J. (eds.) ICGI 1994. LNCS (LNAI), vol. 862, Springer, Heidelberg (1994)
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, p. 211. Springer, Heidelberg (1998)
Cruz, P., Vidal, E.: Modeling Musical Style Using Grammatical Inference Techniques: a Tool for Classifying and Generating Melodies. In: Proceedings of the 3rd International Conference on Web Delivering of Music (WEDELMUSIC 2003), Leeds, UK, Septermber 2003, IEEE Computer Society, Los Alamitos (2003) (to appear)
Forney, G.D.: The Viterbi algorithm. IEEE Proc. 3, 268–278 (1973)
García, P., Vidal, E.: Inference of K-Testable Languages In the Strict Sense and Application to Syntactic Pattern Recognition. IEEE Trans. on PAMI 12(9), 920–925 (1990)
Gold, E.M.: Language Identification in the Limit. Inf. and Control 10, 447–474 (1967)
Jelinek, F.: Statistical Methods for Speech Recognition. MIT Press, Cambridge (1998)
Oncina, J., García, P.: Inferring regular languages in polynomial update time. In: Pérez de la Blanca, N., Sanfeliú, A., Vidal, E. (eds.) Pattern Recognition and Image Analysis, World Scientific, Singapore (1992)
Pollastri, E., Simoncelli, G.: Classification of Melodies by Composer with Hidden Markov Models. In: Proceedings of the First International Conference on WEB Delivering of Music WEDELMUSIC 2001, pp. 88–95. IEEE Computer Press, Los Alamitos (2001)
Ponce de León, P.J., Iñesta, J.M.: Musical Style Identification Using Self-Organising Maps. In: Proceedings of the Second International Conference on WEB Delivering of Music WEDELMUSIC 2002, pp. 82–92. IEEE Computer Press, Los Alamitos (2002)
Rosenfeld, R., Clarkson, P.: Statistical Language Modeling using the CMU Cambridge Toolkit. In: Proceedings of the Eurospeech 1997, vol. 5, pp. 2707–2710 (1997)
Rulot, H., Vidal, E.: Modelling (sub)string-length based constraints through a Gramatical Inference method. In: Pattern Recognition Theory and Applications. NATO ASI Series, vol. F30, pp. 451–459. Springer, Heidelberg (1987)
Segarra, E.: Una Aproximación Inductiva a la Comprensión del Discurso Continuo. PhD diss. Facultad de Informática. Universidad Politécnica de Valencia (1993)
Selfridge-Field, E.: Representing Musical Information for Retrieval. In: Invited talk, Association for Computing Machinery: SIGIR: Exploratory Workshop on Music Information Retrieval, Berkeley, CA, August 19 (1998)
Soltau, H., et al.: Recognition of Music Types. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing ICASSP 1998, Seattle, Washington, pp. 1137–1140 (1998)
Wiggins, G., et al.: A Framework for the Evaluation of Music Representation Systems. Computer Music Journal 17(3), 31–42 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cruz-Alcázar, P.P., Vidal-Ruiz, E., Pérez-Cortés, J.C. (2003). Musical Style Identification Using Grammatical Inference: The Encoding Problem. In: Sanfeliu, A., Ruiz-Shulcloper, J. (eds) Progress in Pattern Recognition, Speech and Image Analysis. CIARP 2003. Lecture Notes in Computer Science, vol 2905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24586-5_46
Download citation
DOI: https://doi.org/10.1007/978-3-540-24586-5_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20590-6
Online ISBN: 978-3-540-24586-5
eBook Packages: Springer Book Archive
