Automatic chord recognition based on probabilistic integration of acoustic features, bass sounds, and chord transition

Katsutoshi Itoyama; Tetsuya Ogata; Hiroshi G. Okuno

doi:10.1007/978-3-642-31087-4_7

Automatic chord recognition based on probabilistic integration of acoustic features, bass sounds, and chord transition

Katsutoshi Itoyama^*, Tetsuya Ogata, Hiroshi G. Okuno

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

We have developed a method that identifies musical chords in polyphonic musical signals. As musical chords mainly represent the harmony of music and are related to other musical elements such as melody and rhythm, we should be able to recognize chords more effectively if this interrelationship is taken into consideration. We use bass pitches as clues for improving chord recognition. The proposed chord recognition system is constructed based on Viterbi-algorithm- based maximum a posteriori estimation that uses a posterior probability based on chord features, chord transition patterns, and bass pitch distributions. Experimental results with 150 Beatles songs that has keys and no modulation showed that the recognition rate was 73.7% on average.

Original language	English
Title of host publication	Advanced Research in Applied Artificial Intelligence - 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Proceedings
Pages	58-67
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-31087-4_7
Publication status	Published - 2012
Externally published	Yes
Event	25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012 - Dalian, China Duration: 2012 Jun 9 → 2012 Jun 12

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7345 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012
Country/Territory	China
City	Dalian
Period	12/6/9 → 12/6/12

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-31087-4_7

Cite this

Itoyama, K., Ogata, T., & Okuno, H. G. (2012). Automatic chord recognition based on probabilistic integration of acoustic features, bass sounds, and chord transition. In Advanced Research in Applied Artificial Intelligence - 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Proceedings (pp. 58-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7345 LNAI). https://doi.org/10.1007/978-3-642-31087-4_7

Automatic chord recognition based on probabilistic integration of acoustic features, bass sounds, and chord transition. / Itoyama, Katsutoshi; Ogata, Tetsuya; Okuno, Hiroshi G.
Advanced Research in Applied Artificial Intelligence - 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Proceedings. 2012. p. 58-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7345 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Itoyama, K, Ogata, T & Okuno, HG 2012, Automatic chord recognition based on probabilistic integration of acoustic features, bass sounds, and chord transition. in Advanced Research in Applied Artificial Intelligence - 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7345 LNAI, pp. 58-67, 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Dalian, China, 12/6/9. https://doi.org/10.1007/978-3-642-31087-4_7

Itoyama K, Ogata T, Okuno HG. Automatic chord recognition based on probabilistic integration of acoustic features, bass sounds, and chord transition. In Advanced Research in Applied Artificial Intelligence - 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Proceedings. 2012. p. 58-67. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-31087-4_7

Itoyama, Katsutoshi ; Ogata, Tetsuya ; Okuno, Hiroshi G. / Automatic chord recognition based on probabilistic integration of acoustic features, bass sounds, and chord transition. Advanced Research in Applied Artificial Intelligence - 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Proceedings. 2012. pp. 58-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We have developed a method that identifies musical chords in polyphonic musical signals. As musical chords mainly represent the harmony of music and are related to other musical elements such as melody and rhythm, we should be able to recognize chords more effectively if this interrelationship is taken into consideration. We use bass pitches as clues for improving chord recognition. The proposed chord recognition system is constructed based on Viterbi-algorithm- based maximum a posteriori estimation that uses a posterior probability based on chord features, chord transition patterns, and bass pitch distributions. Experimental results with 150 Beatles songs that has keys and no modulation showed that the recognition rate was 73.7% on average.",

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AB - We have developed a method that identifies musical chords in polyphonic musical signals. As musical chords mainly represent the harmony of music and are related to other musical elements such as melody and rhythm, we should be able to recognize chords more effectively if this interrelationship is taken into consideration. We use bass pitches as clues for improving chord recognition. The proposed chord recognition system is constructed based on Viterbi-algorithm- based maximum a posteriori estimation that uses a posterior probability based on chord features, chord transition patterns, and bass pitch distributions. Experimental results with 150 Beatles songs that has keys and no modulation showed that the recognition rate was 73.7% on average.

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Automatic chord recognition based on probabilistic integration of acoustic features, bass sounds, and chord transition

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