Journal of KIISE:Computing Practices and Letters (한국정보과학회논문지:컴퓨팅의 실제 및 레터)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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A Query by Humming System Using Humming Algebra

허밍 대수를 이용한 허밍 질의처리 시스템

  • 신제용 (경북대학교 컴퓨터공학과) ;
  • 한욱신 (경북대학교 컴퓨터공학과) ;
  • 이종학 (대구카톨릭대학교 컴퓨터공학과)
  • Published : 2009.08.15
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Abstract

Query by humming is an effective and intuitive querying mechanism when a user wants to find a song without knowing lyrics. The query by humming system takes a user-hummed melody as input, compares it with melodies in a music database, and returns top-k similar melodies to the input. In this paper, we propose a novel algebra for query by humming, and design and implement a real query by humming system called HummingBase by exploiting the algebra. By analyzing existing similarity search techniques, we derive 10 core operators for the algebra. By using the well-defined algebra, we can easily implement such a system in a extensible and modular way. With two case studies, we show that the proposed algebra can easily represent the query processing processes of existing query-by-humming systems.

허밍 질의는 사용자가 가사를 모를 때, 유용하고 직관적으로 사용할 수 있는 질의 방법이다. 허영 질의 시스템은 사용자 멜로디를 입력으로 받고, 이 멜로디를 음악 데이터베이스의 모든 멜로디와 비교하며, 가장 유사한 k개의 멜로디를 반환한다. 본 논문에서는 허영 질의 시스템을 위한 허밍 대수를 제안하고, 허밍 대수를 이용하여 실제 허밍 질의처리 시스템인 HummingBase를 설계하고 구현하였다. 기존 유사 검색 방법들을 분석함으로써 10개의 기본 연산자로 구성된 대수를 유도하였다. 제안한 허밍 대수는 허밍 질의 시스템이 확장이 가능하고 모듈화가 되게끔 구현하는데 사용 될 수 있다. 본 논문에서는 두 가지 사례 연구를 통해, 제안한 허밍 대수를 이용하면 기존의 허밍 질의처리 시스템을 쉽고 편리하게 표현할 수 있음을 보인다.

Keywords

References

  1. Ghias, A., Logan, J., Chmberlin, D., and Smith, C., "Query by humming: Musical information retrieval in an audio database," In ACM Multimedia 1995, pp.231-236, 1995 https://doi.org/10.1145/217279.215273
  2. Kosugi, N., Sakurai, Y., and Morimoto, M., “SoundCompass: A Practical Query-by-Humming System,” In Proceedings of ACM SIGMOD, pp.881-886, 2004 https://doi.org/10.1145/1007568.1007677
  3. Uitdenbgerd, A., and Zobel, J., "Melodic matching techniques for large music databases," In ACM Multimedia 99, pp.57-66, 1999 https://doi.org/10.1145/319463.319470
  4. Zhu, Y., and Shasha, D,, “Warping indexes with envelope transforms for query by humming,” In Proceedings of ACM SIGMOD, pp.181-192, June 2003 https://doi.org/10.1145/872757.872780
  5. Agrawal, R., Faloutsos, C., and Swami, A., "Effi-cient similarity search in sequence databases," In Proc. the 4th Int'l Conf. on Foundations of Data Organization and Algorithms, pp.69-84, 1993 https://doi.org/10.1007/3-540-57301-1
  6. Faloutsos, C., Ranganathan, M., and Manolopoulos, Y., “Fast Subsequence Matching in Time-Series Databases,” In Proc. Int'l Conf. on Management of Data, ACM SIGMOD, Minneapolis, Minnesota, pp.419-429, May 1994 https://doi.org/10.1145/191843.191925
  7. Moon, Y., Whang, K., and Han, W., "General Match: A Subsequence Matching Method in Time-Series Databases Based on Generalized Windows," In Proc. Int'l Conf. on Management of Data, ACM SIGMOD, Madison, Wisconsin, pp.382-393, June 2002 https://doi.org/10.1145/564691.564735
  8. Keogh, E., “Exact indexing of dynamic time warping,” In Proceedings of VLDB, pp.406-417, August 2002
  9. Keogh, E., Chakrabarti, K., Pazzani, M., and Mehrotra, S., “Dimensionality reduction for fast similarity search in large time series databases,” Journal of Knowledge and Information Systems, pp.263-286, 2000 https://doi.org/10.1007/PL00011669
  10. Faloutsos, C., Lin, K., “FastMap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets,” SIGMOD Conference, pp.163-174, 1995 https://doi.org/10.1145/223784.223812
  11. Seidl, T., and Kriegel, H., “Optimal multi-step k-nearest neighbor search,” SIGMOD Conference, pp.154-165, 1998 https://doi.org/10.1145/276304.276319
  12. Standard MIDI Files 1.0, http://jedi.ks.uiuc.edu/~johns/links/music/midifile.html
  13. J.D.Koftinoff Software, ltd. C++ MIDI Library - jdkmidi class library documentation, 2004
  14. Maher, R., and Beauchamp, J., “Fundamental fre-quency estimation of musical signals using a two-way mismatch procedure,” Journal of the Acou-stical Society of America, vol.95, no.4, pp. 2254- 2263, 1994 https://doi.org/10.1121/1.408685
  15. Kosugi, N., Nishihara, Y., Sakata, T., Yamamuro, M., and Kushima, K., “A Practical Query-By-Humming System for a Large Music Database,” In Proc. of the 8th ACM International Conference on Multimedia, pp.333-342, 2000 https://doi.org/10.1145/354384.354520
  16. Rodger McNab, "INTERACTIVE APPLICATIONS OF MUSIC TRANSCRIPTION," Master's thesis, Computer Science at the University of Waikato, 1996