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Whistle for music: using melody transcription and approximate string matching for content-based query over a MIDI database

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Abstract

In this paper, we present a “Whistle for Music” system which enables users to retrieve MIDI format music by whistling a melodic fragment. Three essential components are query processing, MIDI preprocessing and an approximate search engine. For query processing, we have achieved a real-time and robust whistle-to-MIDI converter. For feature extraction, the proposed MIDI preprocessing can extract individual, local and global melodic descriptions from MIDI files. In order to match query with target, we extend an existing search engine into a fast approximate melodic matching engine. Based on the integration of those three components, the system can return a list of MIDI files that are ranked by how closely they match the whistling. The systematic evaluation for the query-by-whistling system is finally performed. The results show that careful measurement and objective comparisons can lead us to know the scaling trend about query and target. One encouraging aspect is that the performance can be predicted based on the evaluation methods.

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References

  1. Blackburn S, DeRoure D (1998) A tool for content based navigation of music. In: Proc ACM Multimedia 98:361–368

  2. Dannenberg RB, Birmingham WP, Tzanetakis G, Meek C, Hu N, Pardo B (2004) The MUSART testbed for query-by-humming evaluation 28(2):34–48

  3. Dixon S (1999) A beat tracking system for audio signals. In: Proceedings of the Diderot Forum on Mathematics and Music, Austrian Computer Society, pp 101–110

  4. Dowling WJ (1978) Scale and contour: two components of a theory of memory for melodies. Psychol Rev 85(4):341–354

    Article  Google Scholar 

  5. Downie JS, Nelson M (2000) Evaluation of a simple and effective music information retrieval method. In: Proc. 23rd ACM Conf. on Research and Development in Information Retrieval (SIGIR ’00), Athens, Greece, pp 73–80

  6. Ghias A, Logan J, Chamberlin D, Smith BC (1995) Query by humming: musical information retrieval in an audio database. In: Proc. of ACM Multimedia, pp 231–236

  7. Harrison M. (1999) Contemporary music theory: level one. Hal Leonard. January

  8. Huron D, Sapp CS, Aarden B (2000) Themefinder. http://www.themefinder.org

  9. Jean TS (1992) The pragmatics of information retrieval experimentation. Inf Process Manag 28(4):467490

    Article  Google Scholar 

  10. Kline RL, Glinert EP (2003) Approximate matching algorithms for music information retrieval using vocal input. Proceedings of the Eleventh ACM International Conference on Multimedia, pp 130–139

  11. Kornstadt A (1998) Themefinder—a web-based melodic search tool. Computing in musicology 11. MIT Press, Cambridge, MA, pp 231–234

    Google Scholar 

  12. Lie L, Muyuan W, Hong-Jiang Z (2004) Repeating pattern discovery and structure analysis from acoustic music data. Proceedings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp 275–282

  13. Lu L, You H, Zhang HJ (2001) A new approach to query by Humming in music retrieval. In: Proceedings of the IEEE International Conference on Multimedia and Expo

  14. McNab RJ, Smith LA, Witten IH, Henderson CL (2000) Tune retrieval in the multimedia library. Multimed Tools Appl 10(2–3):113–132

    Article  MATH  Google Scholar 

  15. McNab RJ, Smith LA, Witten IH, Henderson CL, Cunningham SJ (1996) Towards the digital music library: tune retrieval from acoustic input. Proc. ACM Digital Libraries, Bethesda, pp 11–18

  16. Meek C, Birmingham WP (2001) Thematic extractor. Second Annual International Symposium on Music Information Retrieval. Indiana University, Bloomington, pp 119–128

  17. MeNab RJ, Smith LA, Bainbridge D, Witten IH (1997) The New Zealand digital library melody index http://www.dlib.org/dlib/may97/meldex/OSwritten.html, May

  18. Parsons D (1975) The directory of tunes and musical themes. Spencer Brown, Cambridge

    Google Scholar 

  19. Prechelt L, Typke R (2001) An interface for melody input. ACM Trans Comput-Hum Interact 8(2):133–149

    Article  Google Scholar 

  20. Tao D, Liu H, Tang X (2004) K-BOX: a query-by-singing based music retrieval system. Proceedings of the 12th Annual ACM International Conference on Multimedia, pp 464–467

  21. Uitdenbgerd AL, Schyndel RG (2002) A review of factors affecting music recommender success. In: Fingerhut M (ed) Third International Conference on Music Information Retrieval, Paris, France, pp 204–208

  22. Uitdenbgerd AL, Zobel J (1999) Melodic matching techniques for large music databases. Proceedings of the Seventh ACM International Conference on Multimedia pp 57–66

  23. Unal, E. Narayanan SS, Chew E (2004) A statistical approach to retrieval under user-dependent uncertainty in query-by-humming systems. Proceedings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp 113–118

  24. Wild J (1996) A review of the humdrum toolkit: UNIX tools for musical research, created by David Huron. Music Theory Online 2(7)

  25. Wu S, Manber U (1992) Fast text searching allowing errors. Commun ACM 35:83–91

    Article  Google Scholar 

  26. Tonta Y (1992) Analysis of search failures in document retrieval systems: a review. Public-Access Comput Syst Rev 3(2):4–53

    Google Scholar 

  27. Yip CL, Kao B (1999) A study on musical features for melody databases. in Proc. l0th International Conference on Database and Expert Systems Applications, pp 724–733

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Correspondence to Hung-Che Shen.

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Shen, HC., Lee, C. Whistle for music: using melody transcription and approximate string matching for content-based query over a MIDI database. Multimed Tools Appl 35, 259–283 (2007). https://doi.org/10.1007/s11042-007-0128-5

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  • DOI: https://doi.org/10.1007/s11042-007-0128-5

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