Abstract
Similarity computation is a difficult issue in music information retrieval, because it tries to emulate the special ability that humans show for pattern recognition in general, and particularly in the presence of noisy data. A number of works have addressed the problem of what is the best representation for symbolic music in this context. The tree representation, using rhythm for defining the tree structure and pitch information for leaf and node labeling has proven to be effective in melodic similarity computation. In this paper we propose a solution when we have melodies represented by trees for the training but the duration information is not available for the input data. For that, we infer a probabilistic context-free grammar using the information in the trees (duration and pitch) and classify new melodies represented by strings using only the pitch. The case study in this paper is to identify a snippet query among a set of songs stored in symbolic format. For it, the utilized method must be able to deal with inexact queries and efficient for scalability issues.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bernabeu, J.F., Calera-Rubio, J., Iñesta, J.M., Rizo, D.: A probabilistic approach to melodic similarity. In: Proceedings of MML 2009, pp. 48–53 (2009)
Chappelier, J.-C., Rajman, M.: A generalized cyk algorithm for parsing stochastic cfg. In: TAPD, pp. 133–137 (1998)
Charniak, E.: Tree-bank grammars. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 1031–1036 (1996)
Stephen Downie, J.: Evaluating a Simple Approach to Music Information Retrieval: Conceiving Melodic n-grams as Text. PhD thesis, University of Western Ontario (1999)
Frazier, L., Rayner, K.: Making and correcting errors during sentence comprehension: Eye movements in the analysis of structurally ambiguous sentences. Cognitive Psychology 14(2), 178–210 (1982)
Illescas, P.R., Rizo, D., Iñesta, J.M.: Harmonic, melodic, and functional automatic analysis. In: Proc. of the 2007 International Computer Music Conference, vol. I, pp. 165–168 (2007)
Knuutila, T.: Inference of k-testable tree languages. In: Bunke, H. (ed.) Advances in Structural and Syntactic Pattern Recognition (Proc. of the S+SSPR 1992). World Scientific, Singapore (1993)
Ney, H., Essen, U., Kneser, R.: On the estimation of small probabilities by leaving-one-out. IEEE Trans. Pattern Anal. Mach. Intell. 17(12), 1202–1212 (1995)
Rico-Juan, J.R., Calera-Rubio, J., Carrasco, R.C.: Smoothing and compression with stochastic k-testable tree languages. Pattern Recognition 38(9), 1420–1430 (2005)
Rizo, D., Lemström, K., Iñesta, J.M.: Tree representation in combined polyphonic music comparison. In: Ystad, S., Kronland-Martinet, R., Jensen, K. (eds.) CMMR 2008. LNCS, vol. 5493, pp. 177–195. Springer, Heidelberg (2009)
Stolcke, A.: An efficient probabilistic context-free parsing algorithm that computes prefix probabilities. Computational Linguistics 21, 165–201 (1995)
Verdu-Mas, J.L., Carrasco, R.C., Calera-Rubio, J.: Parsing with probabilistic strictly locally testable tree languages. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(7), 1040–1050 (2005)
Zalcstein, Y.: Locally testable languages. J. Comput. Syst. Sci. 6(2), 151–167 (1972)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bernabeu, J.F., Calera-Rubio, J., Iñesta, J.M. (2011). Classifying Melodies Using Tree Grammars. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_71
Download citation
DOI: https://doi.org/10.1007/978-3-642-21257-4_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21256-7
Online ISBN: 978-3-642-21257-4
eBook Packages: Computer ScienceComputer Science (R0)