A practical query-by-humming system for a large music database

  • Authors:

  • Naoko Kosugi;Yuichi Nishihara;Tetsuo Sakata;Masashi Yamamuro;Kazuhiko Kushima

  • Affiliations:

  • NTT Laboratories, 1-1, Hikarinooka, Yokosuka-shi, Kanagawa, 239-0847, Japan;NTT Laboratories, 1-1, Hikarinooka, Yokosuka-shi, Kanagawa, 239-0847, Japan;NTT Laboratories, 1-1, Hikarinooka, Yokosuka-shi, Kanagawa, 239-0847, Japan;NTT Laboratories, 1-1, Hikarinooka, Yokosuka-shi, Kanagawa, 239-0847, Japan;NTT Laboratories, 1-1, Hikarinooka, Yokosuka-shi, Kanagawa, 239-0847, Japan

  • Venue:
  • MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
  • Year:
  • 2000

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Abstract

A music retrieval system that accepts hummed tunes as queries is described in this paper. This system uses similarity retrieval because a hummed tune may contain errors. The retrieval result is a list of song names ranked according to the closeness of the match. Our ultimate goal is that the correct song should be first on the list. This means that eventually our system's similarity retrieval should allow for only one correct answer.The most significant improvement our system has over general query-by-humming systems is that all processing of musical information is done based on beats instead of notes. This type of query processing is robust against queries generated from erroneous input. In addition, acoustic information is transcribed and converted into relative intervals and is used for making feature vectors. This increases the resolution of the retrieval system compared with other general systems, which use only pitch direction information.The database currently holds over 10,000 songs, and the retrieval time is at most one second. This level of performance is mainly achieved through the use of indices for retrieval. In this paper, we also report on the results of music analyses of the songs in the database. Based on these results, new technologies for improving retrieval accuracy, such as partial feature vectors and or'ed retrieval among multiple search keys, are proposed. The effectiveness of these technologies is evaluated quantitatively, and it is found that the retrieval accuracy increases by more than 20% compared with the previous system [9]. Practical user interfaces for the system are also described.