Skip to main content

Error Tolerant Melody Matching Method in Music Information Retrieval

  • Conference paper
  • 248 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3094))

Abstract

This paper describes a music information retrieval system which uses humming as the key for retrieval. Humming is an easy way for the user to input a melody. However, there are several problems with humming that degrade the retrieval of information. One problem is a human factor. Sometimes people do not sing accurately, especially if they are inexperienced or unaccompanied. Another problem arises from signal processing. Therefore, a music information retrieval method should be sufficiently robust to surmount various humming errors and signal processing problems. A retrieval system has to extract pitch from the user’s humming. However, pitch extraction is not perfect. It often captures half or double pitches, even if the extraction algorithms take the continuity of pitch into account. Considering these problems, we propose a system that takes multiple pitch candidates into account. In addition to the frequencies of the pitch candidates, the confidence measures obtained from their powers are taken into consideration as well. We also propose the use of a query engine with three dimensions that is an extension of the conventional DP algorithm, so that multiple pitch candidates can be treated. Moreover, in the proposed algorithm, DP paths are changed dynamically to take relative spans and pitches of input and reference notes into account in order to treat split or union of notes. In an evaluation experiment, in which the performance of a conventional system was compared with that of the proposed system, better retrieval results were obtained for the latter. Finally, we implemented a GUI based music information retrieval system.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Kageyama, T., Mochizuki, K., Takashima, Y.: Melody Retrieval with Humming. In: Proc. Int. Computer Music Conference (1993)

    Google Scholar 

  2. A.Ghias, J. Logan, D. Chamberlin and B. C. Smith, “Query By Humming: Musical Information Retrieval in an Audio Database,” In Proc. ACM Multimedia, 1995.

    Google Scholar 

  3. Sonoda, T., Goto, M., Muraoka, Y.: WWW-based Music Retrieval System. In: Proc. ICMC 1998, pp. 343–352 (1998)

    Google Scholar 

  4. Shirokaze, T., Makino, S., Kido, K.: Extraction of Fundamental Frequency Using Temporal Continuity over an Input Speech. Trans. IEICE 73-A(9), 1537–1539 (1990)

    Google Scholar 

  5. Feng, D., Siu, W.C., Zhang, H.: Multimedia Information Retrieval and Management: Technological Fundamentals and Applications. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  6. Klapuri, A.: Pitch Estimation Using Multiple Independent Time-Frequency Windows. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 115–118 (1999)

    Google Scholar 

  7. Wei, C., Barry, V.: Folk Music Classification Using Hidden Markov Models. In: Proc. International Symposium on Music Information Retrieval (October 2000)

    Google Scholar 

  8. Pauws, S.: CubyHum: A Fully Operational Query by Humming System. In: Proc. ISMIR 2002 (2002)

    Google Scholar 

  9. Coden, A.R., Brown, E.W., Srinivasan, S. (eds.): SIGIR-WS 2001. LNCS, vol. 2273. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  10. Rabiner, L.R., Juang, B.H.: Fundamentals of Speech Recognition. Prentice-Hall, Englewood Cliffs (1993)

    Google Scholar 

  11. Hashiguchi, H., Nishimura, T., Takita, J., Xin Zhang, J., Oka, R.: Music Signal Spotting Retrieval by a Humming Query. SCI 2001 VII, 280–284 (2001)

    Google Scholar 

  12. Paulus, J., Klapuri, A.: Measuring the Similarity of Rhythmic Patterns. In: Proc. ISMIR 2002 (2002)

    Google Scholar 

  13. MiDiLiB, University of Bonn, Web pages see http://www-mmdb.iai.uni-bonn.de/forschungprojekte/midilib/english/

  14. McNab, R.J., Smith, L.A., Witten, I.H., Henderson, C.L., Sunningham, S.J.: Toward the digital music library: tune retrieval from acoustic input. In: Proc. ACM Digital Libraries, Bethesda (1996)

    Google Scholar 

  15. Themefinder, Stanford University, Web pages see, http://www.themefinder.org/

  16. TuneServer, University of Karlsruhe, Web pages see http://name-this-tune.com/

  17. Roger Jang, J.S., Lee, H., Chen, J.: Super MBox: An Efficient/Effective Content-based Music Retrieval System. In: the ninth ACM Multimedia Conference (Demo paper), pp. 636-637 (2001)

    Google Scholar 

  18. Kosugi, N., Nishihara, Y., Sakata, T., Yamamuro, M., Kushima, K.: A Practical Query-By-Humming System for a Large Music Database. ACM Multimedia 2000, 333–342 (2000)

    Google Scholar 

  19. Heo, S.-P., Suzuki, M., Ito, A., Makino, S.: Three Dimensional Continuous DP Algorithm for Multiple Pitch Candidates in Music Information Retrieval System. Proc. ISMIR 2003 (2003)

    Google Scholar 

  20. McNab, R.J., Smith, L.A., Witten, I.H.: Signal Processing for Melody Transcription. In: Proc. of the 19th Australasian Computer Science Conference (1996)

    Google Scholar 

  21. Nishimura, T., Hashiguchi, H., Takita, J., Zhang, J.X., Goto, M., Oka, R.: Music Signal Spotting Retrieval by a Humming Query Using Start Frame Feature Dependent Continuous Dynamic Programming. In: Proc. ISMIR 2001, pp.211-218 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Heo, SP., Suzuki, M., Ito, A., Makino, S., Chung, HY. (2004). Error Tolerant Melody Matching Method in Music Information Retrieval. In: Nürnberger, A., Detyniecki, M. (eds) Adaptive Multimedia Retrieval. AMR 2003. Lecture Notes in Computer Science, vol 3094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25981-7_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-25981-7_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22163-0

  • Online ISBN: 978-3-540-25981-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics