Abstract
Automatic extraction of frequent repeated patterns in music material is an interesting problem. This paper presents an effective approach of unsupervised frequent pattern discovery method from symbolic music sources. Patterns are discovered even if they are transposed. Experiments on some songs suggest that our approach is promising, specially when dealing with songs that include non-exact repetitions.
This is a preview of subscription content, log in via an institution to check access.
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
Cambouropoulos, E., Crawford, T., Iliopoulos, C.S.: Pattern processing in melodic sequences: Challenges, caveats and prospects. Computers and the Humanities 35(1), 9–21 (2001)
Hsu, J.-L., Liu, C.-C., Chen, A.L.: Efficient repeating pattern finding in music databases. In: Proc. ACM 7th Int. Conf. on Information and Knowledge Management, pp. 281–288 (1998)
Rolland, P.-Y.: Discovering patterns in musical sequences. Journal of New Music Research 28(4), 334–350 (1998)
Aucouturier, J.-J., Sandler, M.: Finding repeating patterns in acoustic musical signals: Applications for audio thumbnailing. In: Audio Engineering 22nd Int. Conf. on Virtual, Synthetic and Entertainment Audio (AES22), pp. 412–421 (2002)
Chu, S., Logan, B.: Music summary using key phrases. In: IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP 2000 (2002)
Meredith, D., Lemström, K., Wiggins, G.A.: Algorithms for discovering repeated patterns in multidimensional representations of polyphonic music. Journal of New Music Research 31(4), 321–345 (2002)
Grachten, M., Arcos, J.L., de Mantaras, R.L.: Melodic similarity: Looking for a good abstraction level, pp. 210–215 (2004)
Dong, G., Pei, J.: Sequence Data Mining (Advances in Database Systems). Springer, New York (2007)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco (2005)
Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)
Pei, J., Han, J., Mortazavi-asl, B., Pinto, H., Chen, Q., Dayal, U., chun Hsu, M.: Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: International Conference on Data Engineering, pp. 215–224 (2001)
Wang, W., Yang, J., Yu, P.S.: Meta-patterns: revealing hidden periodic patterns. IBM Research Report, pp. 550–557 (2001)
Yang, J., Wang, W., Yu, P.S.: Infominer: mining surprising periodic patterns. In: Proceedings of the 7th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 395–400. ACM, New York (2001)
Lawrence, C.E., Altschul, S.F., Boguski, M.S., Liu, J.S., Neuwald, A.F., Wootton, J.C.: Detecting subtle sequence signals: a gibbs sampling strategy for multiple alignment. Science 262(5131), 208–214 (1993)
Jimenez, A., Berzal, F., Cubero, J.C.: Mining induced and embedded subtrees in ordered, unordered, and partially-ordered trees. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds.) Foundations of Intelligent Systems. LNCS (LNAI), vol. 4994, pp. 111–120. Springer, Heidelberg (2008)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)
Zaki, M.J.: Efficiently mining frequent embedded unordered trees. Fundamenta Informaticae 66(1-2), 33–52 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Berzal, F., Fajardo, W., Jiménez, A., Molina-Solana, M. (2009). Mining Musical Patterns: Identification of Transposed Motives. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds) Foundations of Intelligent Systems. ISMIS 2009. Lecture Notes in Computer Science(), vol 5722. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04125-9_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-04125-9_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04124-2
Online ISBN: 978-3-642-04125-9
eBook Packages: Computer ScienceComputer Science (R0)