Automatic Chord Recognition Based on Probabilistic Integration of Acoustic Features, Bass Sounds, and Chord Transition

  • Katsutoshi Itoyama
  • Tetsuya Ogata
  • Hiroshi G. Okuno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7345)


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.


Recognition Rate Acoustic Feature Musical Piece Probabilistic Integration Music Information Retrieval 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Katsutoshi Itoyama
    • 1
  • Tetsuya Ogata
    • 1
  • Hiroshi G. Okuno
    • 1
  1. 1.Kyoto UniversityJapan

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