Advertisement

Query Similar Music by Correlation Degree

  • Feng Yahzong
  • Zhuang Yueting
  • Pan Yunhe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2195)

Abstract

We present in this paper a novel system for query by humming, our method differs from other ones in the followings: Firstly, we use recurrent neural network as the index of music database. Secondly, we present correlation degree to evaluate the music matching precision. We now hold a database of 201 pieces of music with various genres. The result of our experiment reports that the successful rate is 63% with top one matching and 87% with top three matching. Future work will be on melody extraction technique from popular formats of music and on-line music retrieval.

Keywords

Recurrent Neural Network Correlation Degree Pitch Contour Pitch Period Acoustic Input 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Deng J. L.: Control problems of Grey System. Syst. Contr. Let..vol. 5(1982) 288-94Google Scholar
  2. 2.
    Elman, L. J.: Finding Structure in Time. Cognitive Science, 14(1990) 179–211CrossRefGoogle Scholar
  3. 3.
    Ghias, A., Logan, J., Chamberlin, D., and Smith, B. C.: Query by Humming-Musical Information Retrieval in an Audio Database. In Proc. of ACM Multimedia‘95(1995)Google Scholar
  4. 4.
    Gold, B. and Rabiner, L. R.: Parallel Processing Techniques for Estimating Pitch Periods of Speech in the Time Domain. J. Acou. Soc. of Am., Vol. 46, No.2, Part 2, August (1969) 442–48CrossRefGoogle Scholar
  5. 5.
    Kosugi, N.: A Practical Query-By-Humming System for a Large Music Database. In Proc. ACM Multimedia‘2000(2000)Google Scholar
  6. 6.
    Kosugi, N., Nishihara, Y., Kon’ya, S., Yamamuro, M., and Kushima K.: Music Retrieval by Humming. In Proc. PACRIM‘99(1999) 404–407Google Scholar
  7. 7.
    McNab, R. J., Smith, L. A., Bainbridge, D. and Witten, I. H.: The New Zealand Digital Library MELody inDEX. Technical Report, D-Lib(1997)Google Scholar
  8. 8.
    MIDI information. http://www.midi.org
  9. 9.
    Muscle Fish LLC. http://www.musclefish.com/
  10. 10.
    OMRAS(Online Music Recognition And Searching). http://www.omras.org
  11. 11.
    Sonoda, T., Goto, M., Muraokal Y.: A WWW-based Melody Retrieval System. ICMC98Google Scholar
  12. 12.
    Uitdenbogerd, A. and Zobel, J.: Melodic Matching Techniques for Large Music Database. In Proc. of ACM Multimedia‘99(1999) 57–66Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Feng Yahzong
  • Zhuang Yueting
    • 1
  • Pan Yunhe
    • 1
  1. 1.Institute of Artificial IntelligenceZhejiang UniversityHangzhouP. R. China

Personalised recommendations