Score following and retrieval based on chroma and octave representation

Wei Ta Chu, Meng Luen Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the studies of effective representation of music signals and music scores, i.e. chroma and octave features, this work conducts score following and score retrieval. To complement the shortage of chromagram representation, energy distributions in different octaves are used to describe tone height information. By transforming music signals and scores into sequences of feature vectors, score following is transformed as a sequence matching problem, and is solved by the dynamic time warping (DTW) algorithm. To conduct score retrieval, we modify the backtracking step of DTW to determine multiple partial matchings between the query and a score. Experimental results show the effectiveness of the proposed features and the feasibility of the modified DTW algorithm in score retrieval.

Original languageEnglish
Title of host publicationAdvances in Multimedia Modeling - 17th International Multimedia Modeling Conference, MMM 2011, Proceedings
Pages229-239
Number of pages11
EditionPART 1
DOIs
Publication statusPublished - 2011 Jan 26
Event17th Multimedia Modeling Conference, MMM 2011 - Taipei, Taiwan
Duration: 2011 Jan 52011 Jan 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6523 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th Multimedia Modeling Conference, MMM 2011
CountryTaiwan
CityTaipei
Period11-01-0511-01-07

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Chu, W. T., & Li, M. L. (2011). Score following and retrieval based on chroma and octave representation. In Advances in Multimedia Modeling - 17th International Multimedia Modeling Conference, MMM 2011, Proceedings (PART 1 ed., pp. 229-239). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6523 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-17832-0_22