Advertisement

Multimedia Tools and Applications

, Volume 32, Issue 1, pp 49–71 | Cite as

Finding maximum-length repeating patterns in music databases

  • Ioannis Karydis
  • Alexandros Nanopoulos
  • Yannis Manolopoulos
Article

Abstract

This paper introduces the problem of discovering maximum-length repeating patterns in music objects. A novel algorithm is presented for the extraction of this kind of patterns from a melody music object. The proposed algorithm discovers all maximum-length repeating patterns using an “aggressive” accession during searching, by avoiding costly repetition frequency calculation and by examining as few as possible repeating patterns in order to reach the maximum-length repeating pattern(s). Detailed experimental results illustrate the significant performance gains due to the proposed algorithm, compared to an existing baseline algorithm.

Keywords

Maximum-length repeating patterns Data mining Theme discovery Music databases 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the 11th IEEE international conference on data engineering (ICDE), Taipei, Taiwan, pp 3–14Google Scholar
  2. 2.
    Alghoniemy M, Tewfik AH (2000) User-defined music sequence retrieval. In: Proceedings of the 8th ACM international conference on multimedia, Los Angeles, CA, pp 356–358Google Scholar
  3. 3.
    Aucouturier J-J, Sandler M (2002) Finding repeating patterns in acoustic musical signals: applications for audio thumbnailing. In: Proceedings 22nd AES international conference on virtual, synthetic and entertainment audio, Espoo, Finland, pp 412–421Google Scholar
  4. 4.
    Bainbridge D, Bernbom G, Davidson MW, Dillon AP, Dovey M, Dunn JW, Fingerhut M, Fujinaga I, Isaacson EJ (2001) Digital music libraries—research and development. In: Proceedings of the 1st ACM/IEEE joint conference on digital libraries (JCDL), Roanoke, VA, pp 446–448Google Scholar
  5. 5.
    Barlow H, Morgenstern S (1975) A dictionary of musical themes. Crown, New YorkGoogle Scholar
  6. 6.
    Bartsch M, Birmingham WP, Bykowski D, Dannenberg RB, Mazzoni D, Meek C, Mellody M, Rand W, Wakefield GH, (2001) MUSART: music retrieval via aural queries. In: Proceedings of the 2nd annual international symposium on music information retrieval (ISMIR), Bloomington, IN, pp 73–81Google Scholar
  7. 7.
    Bayardo R (1998) Efficiently mining long patterns from databases. In: Proceedings of the ACM international conference on management of data (SIGMOD), Seattle, WA, pp 85–93Google Scholar
  8. 8.
    Byrd D, Crawford T (2002) Problems of music information retrieval in the real world. Inf Process Manag 38(2):249–272CrossRefGoogle Scholar
  9. 9.
    Chavez E, Navarro G (2002) A metric index for approximate string matching. In: Proceedings of the 5th Latin American symposium on theoretical informatics (LATIN), New York, NY, pp 181–195Google Scholar
  10. 10.
    Chen ALP, Chang M, Chen J, Hsu J, Hsu C, Hua YS (2000) Query by music segments: an efficient approach for song retrieval. In: Proceedings of the IEEE international conference on multimedia and expo, New York, NY pp 873–876Google Scholar
  11. 11.
    Chen JCC, Chen ALP (1998) Query by rhythm: an approach for song retrieval in music databases. In: Proceedings of the workshop on research issues in data engineering (RIDE), Tucson, AZ, pp 139–146Google Scholar
  12. 12.
    Chen H, Chen ALP (2001) A music recommendation system based on music data grouping and user interests. In: Proceedings of the conference in information and knowledge management (CIKM), Hilton, Singapore, pp 231–238Google Scholar
  13. 13.
    Chuo T-C, Chen ALP, Liu C-C, (1996) Music DataBases: indexing techniques and implementation. In: Proceedings of the international workshop on multimedia databases management systems, Blue Mountain Lake, NY, pp 46–53Google Scholar
  14. 14.
    Crawford T, Iliopoulos CS, Raman R (1998) String matching techniques for music similarity and melodic recognition. Comput Musicol 11:73–100Google Scholar
  15. 15.
    Dovey MJ (2001) Adding content-based searching to a traditional music library catalogue server. In: Proceedings of the 1st ACM/IEEE joint conference on digital libraries (JCDL), Roanoke, VA, pp 249–250Google Scholar
  16. 16.
    Durey AS, Clements MA (2001) Melody spotting using hidden Markov models. In: Proceedings of the 2nd annual international symposium on music information retrieval (ISMIR), Berkeley, CA, pp 109–117Google Scholar
  17. 17.
    Francu C, Nevill-Manning CG (2000) Distance metrics and indexing strategies for a digital library of popular music. In: Proceedings of the IEEE international conference on multimedia and expo, New York, NY, pp 889–894Google Scholar
  18. 18.
    Hamilton JD (1994) Time series analysis. Princeton University Press, Princeton, NJzbMATHGoogle Scholar
  19. 19.
    Hsu J, Liu C, Chen ALP (1998) Efficient repeating pattern finding in music databases. In: Proceedings of the ACM international conference on information and knowledge management (CIKM), Bethesda, MDGoogle Scholar
  20. 20.
    Hsu JL, Liu CC, Chen ALP (2001) Discovering non-trivial repeating patterns in music data. IEEE Trans Multimedia 3(3):311–325CrossRefGoogle Scholar
  21. 21.
    Iliopoulos CS, Kurokawa M (2002) Exact & approximate distributed matching for musical melodic recognition. In: Proceedings of the convention on artificial intelligence and the simulation of behaviour (AISB). Imperial College, London, UK, pp 49–56Google Scholar
  22. 22.
    Iliopoulos CS, Niyad M, Lenstrom K, Pinzon YJ (2002) Evolution of musical motifs in polyphonic passages. In: Proceedings of the convention on artificial intelligence and the simulation of behaviour (AISB). Imperial College, London, pp 67–75Google Scholar
  23. 23.
    Kang YK, Kim YS, Ku KI (2001) Extracting theme melodies by using a graphical clustering algorithm for content-based MIR. In: Proceedings of the 5th East-European conference on advances in databases and information systems (ADBIS), Springer-Verlag, London, pp 84–97Google Scholar
  24. 24.
    Kassler M (1966) Toward musical information retrieval. Perspect New Music 4(2):59–67CrossRefGoogle Scholar
  25. 25.
    Koh JL, Yu WDC (2001) Efficient feature mining in music objects. In: Proceedings of the 12th conference in database and expert system applications (DEXA), London, UK, pp 221–231Google Scholar
  26. 26.
    Kornstadt A (1998) Themefinder: a web-based melodic search tool. Comput Musicol 11:231–236Google Scholar
  27. 27.
    Lin D-I, Kedem Z (2002) Pincer-search: an efficient algorithm for discovering the maximum frequent set. IEEE Trans Knowl Data Eng 14(3):553–566CrossRefGoogle Scholar
  28. 28.
    Liu CC, Hsu JL, Chen ALP (1999) Efficient theme and non-trivial repeating pattern discovering in music databases. In: Proceedings of the 15th IEEE international conference on data engineering (ICDE), Sydney, Australia, pp 14–21Google Scholar
  29. 29.
    Meek C, Birmingham WP (2001) Thematic extractor. In: Proceedings of the 2nd annual international symposium on music information retrieval (ISMIR), Bloomington, IN, pp 119–128Google Scholar
  30. 30.
    Mongeau M, Sankoff D (1990) Comparison of musical sequences. Comput Humanit 24:161–175CrossRefGoogle Scholar
  31. 31.
    Nishimura T, Hashiguchi H, Takita J, Zhang JX, Goto M, Oka R (2001) Music signal spotting retrieval by a humming query suing start frame feature dependent continuous dynamic programming. In: Proceedings of the 2nd annual international symposium on music information retrieval (ISMIR), Bloomington, IN, pp 211–218Google Scholar
  32. 32.
    O’Maidin DS, Cahill M (2001) Score processing for MIR. In: Proceedings of the 2nd annual international symposium on music information retrieval (ISMIR), Bloomington, IN, pp 59–64Google Scholar
  33. 33.
    Park J, Chen M-S, Yu P (1997) Using a hash-based method with transaction trimming for mining association rules. IEEE Trans Knowl Data Eng 9(5):813–825CrossRefGoogle Scholar
  34. 34.
    Pienimaki A (2002) Indexing music databases using automatic extraction of frequent phrases. In: Proceedings of the 3nd annual international symposium on music information retrieval (ISMIR), Paris, France, pp 25–30Google Scholar
  35. 35.
    Pikrakis A, Theodoridis S, Kamarotos D (2002) Recognition of isolated musical patterns using hidden markov models. In: Proceedings of the II international conference on music and artificial intelligence (ICMAI), Edinburg, Scotland, pp 133–143Google Scholar
  36. 36.
    Raphael C (2001) Automated rhythm transcription. In: Proceedings of the 2nd annual international symposium on music information retrieval (ISMIR), Bloomington, IN, pp 99–107Google Scholar
  37. 37.
    Rolland P-Y, Ganascia J-G (2002) Pattern detection and discovery: the case of music data mining. In: Proceedings of the conference on pattern detection and discovery, London, UK, pp 190–198Google Scholar
  38. 38.
    Shifrin J, Pardo B, Meek C, Birmingham W (2002) HMM-based musical query retrieval. In: Proceedings of the 2nd ACM/IEEE-CS conference on digital libraries, Portland, OR, pp 295–300Google Scholar
  39. 39.
    Smith L, Medina R (2001) Discovering themes by exact pattern matching. In: Proceedings of the 2nd annual international symposium on music information retrieval (ISMIR), Bloomington, IN, pp 31–32Google Scholar
  40. 40.
    Takasu A, Yanase T, Kanazawa T, Adachi J (1999) Music structure analusis and its application to theme phrase extraction. In: Third European conference on research and advanced technology for digital libraries, Paris, France, pp 92–105Google Scholar
  41. 41.
    Uitdenbogerd A, Zobel J (1999) Melodic matching techniques for large music databases. In: Proceedings of the ACM international multimedia conference, Orlando, FL, pp 57–66Google Scholar
  42. 42.
    Velivelli A, Zhai C, Huang TS (2003) Audio segment retrieval using a synthesized HMM. In: Proceedings of the ACM SIGIR workshop on multimedia information retrieval, Toronto, CanadaGoogle Scholar
  43. 43.
    Zaki M, Parthasarathy S, Ogihara M, Li W (1997) New algorithms for fast discovery of association rules. In: Proceedings of the international conference on knowledge discovery and data mining (KDD), Menlo Park, CA, pp 283–286Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  • Ioannis Karydis
    • 1
  • Alexandros Nanopoulos
    • 1
  • Yannis Manolopoulos
    • 1
  1. 1.Department of InformaticsAristotle UniversityThessalonikiGreece

Personalised recommendations