Mining transposed motifs in music
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The discovery of frequent musical patterns (motifs) is a relevant problem in musicology. This paper introduces an unsupervised algorithm to address this problem in symbolically-represented musical melodies. Our algorithm is able to identify transposed patterns including exact matchings, i.e., null transpositions. We have tested our algorithm on a corpus of songs and the results suggest that our approach is promising, specially when dealing with songs that include non-exact repetitions.
- Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In 20th int. conf. on very large data bases (pp. 487–499).
- Aucouturier, J. J., & Sandler, M. (2002). 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).
- Bartsch, M., & Wakefield, G. (2005). Audio thumbnailing of popular music using chroma-based representations. IEEE Transactions on Multimedia, 7(1), 96–104. CrossRef
- Berzal, F., Fajardo, W., Jiménez, A., & Molina-Solana, M. (2009). Mining musical patterns: Identification of transposed motives. In 18th Int. symposium of foundations of intelligent systems. Lecture Notes in Computer Science, vol. 5722, pp. 271–280.
- Böckenhauer, H. J., & Bongartz, D. (2007). Algorithmic aspects of bioinformatics. New York: Springer.
- Cambouropoulos, E., Crawford, T., & Iliopoulos, C. S. (2001). Pattern processing in melodic sequences: Challenges, caveats and prospects. Computers and the Humanities, 35(1), 9–21. CrossRef
- Chu, S., & Logan, B. (2002). Music summary using key phrases. In IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP-00) (pp. 749–752).
- Dong, G., & Pei, J. (2007). Sequence data mining (advances in database systems). New York: Springer.
- Grachten, M., Arcos, J. L., & de Mantaras, R. L. (2004). Melodic similarity: Looking for a good abstraction level. In 5th Int. Conf. on Music Information Retrieval (ISMIR 2004) (pp. 210–215).
- Han, J., & Kamber, M. (2005). Data mining: Concepts and techniques. Denver: Morgan Kaufmann.
- Hsu, J. L., Liu, C. C., & Chen, A. (1998). Efficient repeating pattern finding in music databases. In ACM 7th int. conf. on information and knowledge management (pp. 281–288).
- Jiang, L., & Hamilton, H. J. (2003). Methods for mining frequent sequential patterns. In Advances in artificial intelligence, Lecture of Notes in Computer Sciences (Vol. 2671/2003, pp. 486–491). Berlin: Springer.
- Jimenez, A., Berzal, F., & Cubero, J. C. (2009). Mining induced and embedded subtrees in ordered, unordered, and partially-ordered trees. Knowledge and Information Systems, 4994/2008, 111–120. doi:10.1007/s10115-009-0213-3.
- Levy, M., & Sandler, M. (2008). Structural segmentation of musical audio by constrained clustering. IEEE Transactions on Audio, Speech, and Language Processing, 16(2), 318–326. CrossRef
- Meredith, D., Lemström, K., & Wiggins, G. A. (2002). Algorithms for discovering repeated patterns in multidimensional representations of polyphonic music. Journal of New Music Research, 31(4), 321–345 CrossRef
- Narmour, E. (1992). The analysis and cognition of melodic complexity: The implication realization model. Chicago: Univ. Chicago Press.
- Paulus, J., & Klapuri, A. (2009). Music structure analysis using a probabilistic fitness measure and a greedy search algorithm. IEEE Transactions on Audio, Speech, and Language Processing, 17(6), 1159–1170. CrossRef
- Pei, J., Han, J., Asl, M. B., Pinto, H., Chen, Q., Dayal, U., et al. (2001). Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In 5th int. conf. on extending database technology (pp. 215–224).
- Pienimäki, A. (2002). Indexing music databases using automatic extraction of frequent phrases. In 3rd int. conf. on music information retrieval (pp. 25–30).
- Rolland, P. Y. (1998). Discovering patterns in musical sequences. Journal of New Music Research, 28(4), 334–350 CrossRef
- Srikant, R., & Agrawal, R. (1996). Mining sequential patterns: Generalizations and performance improvements. Extending Database Technology, 1057, 3–17.
- Wang, W., Yang, J., & Yu, P. S. (2001). Meta-patterns: Revealing hidden periodic patterns. In IBM research report (pp. 550–557).
- Yang, J., Wang, W., & Yu, P. S. (2001). Infominer: mining surprising periodic patterns. In 7th ACM int. conf. on knowledge discovery and data mining (SIGKDD) (pp. 395–400). New York: ACM CrossRef
- Zaki, M. J. (2001). Spade: an efficient algorithm for mining frequent sequences. Machine Learning, 42, 31–60. CrossRef
- Zaki, M. J. (2005a) Efficiently mining frequent embedded unordered trees. Fundamenta Informaticae, 66(1–2), 33–52
- Zaki, M. J. (2005b). Efficiently mining frequent trees in a forest: Algorithms and applications. IEEE Transactions on Knowledge and Data Engineering, 17(8), 1021–1035. CrossRef
- Zhang, T., & Samadani, R. (2007). Automatic generation of music thumbnails. In Proceedings of the 2007 IEEE int. conf. on multimedia and expo (pp. 228–231).
- Mining transposed motifs in music
Journal of Intelligent Information Systems
Volume 36, Issue 1 , pp 99-115
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