Key estimation using circle of fifths

Takahito Inoshita, Jiro Katto

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

3 Citations (Scopus)

Abstract

This paper presents a novel key estimation method of sound sources based on the music theory known as "circle of fifths". We firstly overview music theory and formulate the musical key analysis by vector operations. In detail, we separate music sources into small pieces and calculate FFT-based chroma vectors. They are converted to tonality vectors and COF (circle-of-fifth) vectors are calculated from the tonality vectors, which are mapped onto the circle of fifths coordinate. As a result, each music source can be represented by traces of COF vectors, which usually stay inside a single key region on the circle of fifths. Finally, HMM is applied to the traces of COF vectors in order to detect keys and their boundaries. Experiments using music databases are also carried out.

Original languageEnglish
Title of host publicationAdvances in Multimedia Modeling - 15th International Multimedia Modeling Conference, MMM 2009, Proceedings
Pages287-297
Number of pages11
DOIs
Publication statusPublished - 2009 Feb 4
Event15th International Multimedia Modeling Conference, MMM 2009 - Sophia-Antipolis, France
Duration: 2009 Jan 72009 Jan 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5371 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Multimedia Modeling Conference, MMM 2009
CountryFrance
CitySophia-Antipolis
Period09/1/709/1/9

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Inoshita, T., & Katto, J. (2009). Key estimation using circle of fifths. In Advances in Multimedia Modeling - 15th International Multimedia Modeling Conference, MMM 2009, Proceedings (pp. 287-297). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5371 LNCS). https://doi.org/10.1007/978-3-540-92892-8_31