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

Authors:
Katsutoshi Itoyama;Tetsuya Ogata;Hiroshi G. Okuno
Affiliations:
Kyoto University, Japan;Kyoto University, Japan;Kyoto University, Japan
Venue:
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Year:
2012

Citing 3
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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.