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Part of the book series: The Information Retrieval Series ((INRE,volume 22))

Abstract

This chapter addresses the problem of the retrieval of music documents from multimedia digital libraries. Some of the peculiarities of the music language are described, showing similarities and differences between indexing and retrieval of textual and music documents. After reviewing the main approaches to music retrieval, a novel methodology is presented, which combines an approximate matching approach with an indexing scheme. The methodology is based on the statistical modeling of musical lexical units with weighted transducers, which are automatically built from the melodic and rhythmic information of lexical units. An experimental evaluation of the methodology is presented, showing encouraging results.

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References

  1. Baeza-Yates, R., Ribeiro-Neto, B. (eds.): Modern Information Retrieval. ACM Press, New York, NY (1999)

    Google Scholar 

  2. Bainbridge, D., Nevill-Manning, C., Witten, I., Smith, L., McNab, R.: Towards a digital library of popular music. In: Proceedings of the ACM Conference on Digital Libraries, pp. 161–169 (1999)

    Google Scholar 

  3. Bengio, Y.: Markovian models for sequential data. Neural Computer Surveys 2, 129–162 (1999)

    Google Scholar 

  4. Berenzweig, A., Logan, B., Ellis, D., Whitman, B.: A large-scale evaluation of acoustic and subjective music-similarity measures. Computer Music Journal 28(2), 63–76 (2004)

    Article  Google Scholar 

  5. Birmingham, W., Dannenberg, R., Wakefield, G., Bartsch, M., Bykowski, D., Mazzoni, D., Meek, C., Mellody, M., Rand, W.: MUSART: Music retrieval via aural queries. In: Proceedings of the International Conference on Music Information Retrieval, pp. 73–82 (2001)

    Google Scholar 

  6. Cantate: Computer Access to Notation and Text in Music Libraries (July 2007). http://projects.fnb.nl/cantate/

    Google Scholar 

  7. Clausen, M., Engelbrecht, R., Meyer, D., Schmitz, J.: PROMS: A web-based tool for searching in polyphonic music. In: Proceedings of the International Symposium of Music Information Retrieval (2000)

    Google Scholar 

  8. Cope, D.: Pattern matching as an engine for the computer simulation of musical style. In: Proceedings of the International Computer Music Conference, pp. 288–291 (1990)

    Google Scholar 

  9. Doraisamy, S., Rüger, S.: A polyphonic music retrieval system using N-grams. In: Proceedings of the International Conference on Music Information Retrieval, pp. 204–209 (2004)

    Google Scholar 

  10. Dowling, W.: Scale and contour: Two components of a theory of memory for melodies. Psychological Review 85(4), 341–354 (1978)

    Article  Google Scholar 

  11. Downie, S., Nelson, M.: Evaluation of a simple and effective music information retrieval method. In: Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR), pp. 73–80 (2000)

    Google Scholar 

  12. Dunn, J., Mayer, C.: VARIATIONS: A Digital Music Library System at Indiana University. In: Proceedings of ACM Conference on Digital Libraries, pp. 12–19 (1999)

    Google Scholar 

  13. Ghias, A., Logan, J., Chamberlin, D., Smith, B.: Query by humming: Musical information retrieval in an audio database. In: Proceedings of the ACM Conference on Digital Libraries, pp. 231–236 (1995)

    Google Scholar 

  14. Giannopoulos, P., Veltkamp, R.: A pseudo-metric for weighted point sets. In: Proceedings of the European Conference on Computer Vision, pp. 715–730 (2002)

    Google Scholar 

  15. Gómez, E., Herrera, P.: Estimating the tonality of polyphonic audio files: Cognitive versus machine learning modelling strategies. In: Proceedings of the International Conference on Music Information Retrieval, pp. 92–95 (2004)

    Google Scholar 

  16. Harte, C., Sandler, M., Abdallah, S., Gómez, E.: Symbolic representation of musical chords: a proposed syntax for text annotations. In: Proceedings of the International Conference on Music Information Retrieval, pp. 66–71 (2005)

    Google Scholar 

  17. Haus, G., Pollastri, E.: A multimodal framework for music inputs. In: Proceedings of the ACM Multimedia Conference, pp. 282–284 (2000)

    Google Scholar 

  18. Hoos, H., Renz, K., Görg, M.: GUIDO/MIR – an experimental musical information retrieval system based on GUIDO music notation. In: Proceedings of the International Symposium on Music Information Retrieval, pp. 41–50 (2001)

    Google Scholar 

  19. Hsu, J.L., Liu, C., Chen, A.: Efficient repeating pattern finding in music databases. In: Proceeding of the International Conference on Information and Knowledge Management, pp. 281–288 (1998)

    Google Scholar 

  20. Hu, N., Dannenberg, R.: A comparison of melodic database retrieval techniques using sung queries. In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, pp. 301–307 (2002)

    Google Scholar 

  21. Hu, N., Dannenberg, R., Lewis, A.: A probabilistic model of melodic similarity. In: Proceedings of the International Computer Music Conference, pp. 509–515 (2002)

    Google Scholar 

  22. Huron, D.: The Humdrum Toolkit: Reference Manual. Center for Computer Assisted Research in the Humanities, Menlo Park, CA (1995)

    Google Scholar 

  23. Jones, K.S., Willett, P.: Readings in Information Retrieval. Morgan Kaufmann, San Francisco, CA (1997)

    Google Scholar 

  24. Krumhansl, C.: Why is musical timbre so hard to understand? In: S. Nielsen, O. Olsson (eds.) Structure and Perception Electroacoustic Sound and Music, pp. 45–53. Elsevier, Amsterdam, NL (1989)

    Google Scholar 

  25. Lee, J., Downie, J.: Survey of music information needs, uses, and seeking behaviours: Preliminary findings. In: Proceedings of the International Conference on Music Information Retrieval, pp. 441–446 (2004)

    Google Scholar 

  26. Lesaffre, M., Leman, M., Tanghe, K., Baets, B.D., Meyer, H.D., Martens, J.P.: User-dependent taxonomy of musical features as a conceptual framework for musical audio-mining technology. In: Proceedings of the Stockholm Music Acoustics Conference, pp. 635–638 (2003)

    Google Scholar 

  27. Lesaffre, M., Tanghe, K., Martens, G., Moelants, D., Leman, M., Baets, B.D., Meyer, H.D., Martens, J.P.: The MAMI query-by-voice experiment: Collecting and annotating vocal queries for music information retrieval. In: Proceedings of the International Conference on Music Information Retrieval, pp. 65–71 (2003)

    Google Scholar 

  28. Lubiw, A., Tanur, L.: Pattern matching in polyphonic music as a weighted geometric translation problem. In: Proceedings of the International Conference of Music Information Retrieval, pp. 289–296 (2004)

    Google Scholar 

  29. MAMI: Musical Audio Mining – “query by humming” (July 2007). http://www.ipem.ugent.be/MAMI/

    Google Scholar 

  30. McLane, A.: Music as information. In: M. Williams (ed.) Arist, Vol. 31, chap. 6, pp. 225–262. American Society for Information Science (1996)

    Google Scholar 

  31. Meek, C., Birmingham, W.: Automatic thematic extractor. Journal of Intelligent Information Systems 21(1), 9–33 (2003)

    Article  Google Scholar 

  32. Melucci, M., Orio, N.: Musical information retrieval using melodic surface. In: Proceedings of the ACM Conference on Digital Libraries, pp. 152–160 (1999)

    Google Scholar 

  33. Melucci, M., Orio, N.: A comparison of manual and automatic melody segmentation. In: Proceedings of International Conference on Music Information Retrieval, pp. 7–14 (2002)

    Google Scholar 

  34. Middleton, R.: Studying Popular Music. Open University Press, Philadelphia, PA (2002)

    Google Scholar 

  35. Mohri, M.: Finite-state transducers in language and speech processing. Computational Linguistics 23(2), 269–311 (1997)

    MathSciNet  Google Scholar 

  36. Musica: The International Database of Choral Repertoire (July 2007). http://www.musicanet.org/

    Google Scholar 

  37. Neve, G., Orio, N.: Indexing and retrieval of music documents through pattern analysis and data fusion techniques. In: Proceedings of the International Conference on Music Information Retrieval, pp. 216–223 (2004)

    Google Scholar 

  38. Orio, N., Neve, G.: Experiments on segmentation techniques for music documents indexing. In: Proceedings of the International Conference on Music Information Retrieval, pp. 104–107 (2005)

    Google Scholar 

  39. Owen, G.: Using connectionist models to explore complex musical patterns. Computer Music Journal 13(3), 67–75 (1989)

    Article  Google Scholar 

  40. Parker, C.: A tree-based method for fast melodic retrieval. In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, pp. 254–255 (2004)

    Google Scholar 

  41. Pienimäki, A.: Indexing music database using automatic extraction of frequent phrases. In: Proceedings of the International Conference on Music Information Retrieval, pp. 25–30 (2002)

    Google Scholar 

  42. Rabiner, L.: A tutorial on hidden Markov models and selected applications. Proceedings of the IEEE 77(2), 257–286 (1989)

    Article  Google Scholar 

  43. Shifrin, J., Pardo, B., Meek, C., Birmingham, W.: HMM-based musical query retrieval. In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, pp. 295–300 (2002)

    Google Scholar 

  44. Tseng, Y.: Content-based retrieval for music collections. In: Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR), pp. 176–182 (1999)

    Google Scholar 

  45. Typke, R., Veltkamp, R., Wiering, F.: Searching notated polyphonic music using transportation distances. In: Proceedings of the ACM International Conference on Multimedia, pp. 128–135 (2004)

    Google Scholar 

  46. Ukkonen, E., Lemström, K., Mäkinen, V.: Geometric algorithms for transposition invariant content-based music retrieval. In: Proceedings of the International Conference of Music Information Retrieval, pp. 193–199 (2003)

    Google Scholar 

  47. Wiggins, G., Lemström, K., Meredith, D.: SIA(M)ESE: An algorithm for transposition invariant, polyphonic content-based music retrieval. In: Proceedings of the International Conference of Music Information Retrieval, pp. 283–284 (2002)

    Google Scholar 

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Orio, N. (2008). Music Indexing and Retrieval for Multimedia Digital Libraries. In: Agosti, M. (eds) Information Access through Search Engines and Digital Libraries. The Information Retrieval Series, vol 22. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75134-2_8

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  • DOI: https://doi.org/10.1007/978-3-540-75134-2_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75133-5

  • Online ISBN: 978-3-540-75134-2

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