Chord Recognition in Symbolic Music Using Semi-Markov Conditional Random Fields
نویسندگان
چکیده
Chord recognition is a fundamental task in the harmonic analysis of tonal music, in which music is processed into a sequence of segments such that the notes in each segment are consistent with a corresponding chord label. We propose a machine learning model for chord recognition that uses semi-Markov Conditional Random Fields (semiCRFs) to perform a joint segmentation and labeling of symbolic music. One benefit of using a semi-Markov model is that it enables the utilization of segment-level features, such as segment purity and chord coverage, that capture the extent to which the events in an entire segment of music are compatible with a candidate chord label. Correspondingly, we develop a rich set of segment-level features for a semi-CRF model that also incorporates the likelihood of a large number of chord-to-chord transitions. Evaluations on a dataset of Bach chorales and a corpus of theme and variations for piano by Beethoven and Mozart show that the proposed semi-CRF model outperforms a discriminatively trained Hidden Markov Model (HMM) that does sequential labeling of sounding events, thus demonstrating the suitability of semi-Markov models for joint segmentation and labeling of music.
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