Error Tolerant Melody Matching Method in Music Information Retrieval

Sung Phil Heo, Motoyuki Suzuki, Akinori Ito, Shozo Makino, Hyun Yeol Chung

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsAndreas Nurnberger, Marcin Detyniecki
PublisherSpringer Verlag
Pages212-227
Number of pages16
ISBN (Print)3540221638
DOIs
Publication statusPublished - 2004 Jan 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3094
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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