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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Itoyama, K., Ogata, T., Okuno, H.G. (2012). Automatic Chord Recognition Based on Probabilistic Integration of Acoustic Features, Bass Sounds, and Chord Transition. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds) Advanced Research in Applied Artificial Intelligence. IEA/AIE 2012. Lecture Notes in Computer Science(), vol 7345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31087-4_7
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DOI: https://doi.org/10.1007/978-3-642-31087-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31086-7
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