Journal of Intelligent Information Systems

, Volume 36, Issue 1, pp 99–115 | Cite as

Mining transposed motifs in music

  • Aída Jiménez
  • Miguel Molina-Solana
  • Fernando Berzal
  • Waldo Fajardo


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.


Musical mining Motifs Frequent pattern mining 



F. Berzal and A. Jiménez are supported by the projects TIN2006-07262 and TIN2009-08296, whereas W. Fajardo and M. Molina-Solana are supported by the research project TIN2006-15041-C04-01.


  1. 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).Google Scholar
  2. 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).Google Scholar
  3. Bartsch, M., & Wakefield, G. (2005). Audio thumbnailing of popular music using chroma-based representations. IEEE Transactions on Multimedia, 7(1), 96–104.CrossRefGoogle Scholar
  4. 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.Google Scholar
  5. Böckenhauer, H. J., & Bongartz, D. (2007). Algorithmic aspects of bioinformatics. New York: Springer.zbMATHGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. 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).Google Scholar
  8. Dong, G., & Pei, J. (2007). Sequence data mining (advances in database systems). New York: Springer.Google Scholar
  9. 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).Google Scholar
  10. Han, J., & Kamber, M. (2005). Data mining: Concepts and techniques. Denver: Morgan Kaufmann.Google Scholar
  11. 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).Google Scholar
  12. 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.Google Scholar
  13. 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.Google Scholar
  14. 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.CrossRefGoogle Scholar
  15. 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), 321–345CrossRefGoogle Scholar
  16. Narmour, E. (1992). The analysis and cognition of melodic complexity: The implication realization model. Chicago: Univ. Chicago Press.Google Scholar
  17. 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.CrossRefGoogle Scholar
  18. 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).Google Scholar
  19. Pienimäki, A. (2002). Indexing music databases using automatic extraction of frequent phrases. In 3rd int. conf. on music information retrieval (pp. 25–30).Google Scholar
  20. Rolland, P. Y. (1998). Discovering patterns in musical sequences. Journal of New Music Research, 28(4), 334–350CrossRefMathSciNetGoogle Scholar
  21. Srikant, R., & Agrawal, R. (1996). Mining sequential patterns: Generalizations and performance improvements. Extending Database Technology, 1057, 3–17.Google Scholar
  22. Wang, W., Yang, J., & Yu, P. S. (2001). Meta-patterns: Revealing hidden periodic patterns. In IBM research report (pp. 550–557).Google Scholar
  23. 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: ACMCrossRefGoogle Scholar
  24. Zaki, M. J. (2001). Spade: an efficient algorithm for mining frequent sequences. Machine Learning, 42, 31–60.zbMATHCrossRefGoogle Scholar
  25. Zaki, M. J. (2005a) Efficiently mining frequent embedded unordered trees. Fundamenta Informaticae, 66(1–2), 33–52zbMATHMathSciNetGoogle Scholar
  26. 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.CrossRefGoogle Scholar
  27. 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).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Aída Jiménez
    • 1
  • Miguel Molina-Solana
    • 1
  • Fernando Berzal
    • 1
  • Waldo Fajardo
    • 1
  1. 1.Centro de Investigación en Tecnologías de la Información y las ComunicacionesUniversity of GranadaGranadaSpain

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