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We present a new approach to harmonic analysis that is trained to segmentmusic into a sequence of chord spans tagged with chord labels. Formulated as asemi-Markov Conditional Random Field (semi-CRF), this joint segmentation andlabeling approach enables the use of a rich set of segment-level features, suchas segment purity and chord coverage, that capture the extent to which theevents in an entire segment of music are compatible with a candidate chordlabel. The new chord recognition model is evaluated extensively on threecorpora of classical music and a newly created corpus of rock music.Experimental results show that the semi-CRF model performs substantially betterthan previous approaches when trained on a sufficient number of labeledexamples and remains competitive when the amount of training data is limited.

Chord Recognition in Symbolic Music: A Segmental CRF Model, Segment-Level Features, and Comparative Evaluations on Classical and Popular Music