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Cadences are complex structures that have been driving music from thebeginning of contrapuntal polyphony until today. Detecting such structures isvital for numerous MIR tasks such as musicological analysis, key detection, ormusic segmentation. However, automatic cadence detection remains challengingmainly because it involves a combination of high-level musical elements likeharmony, voice leading, and rhythm. In this work, we present a graphrepresentation of symbolic scores as an intermediate means to solve the cadencedetection task. We approach cadence detection as an imbalanced nodeclassification problem using a Graph Convolutional Network. We obtain resultsthat are roughly on par with the state of the art, and we present a modelcapable of making predictions at multiple levels of granularity, fromindividual notes to beats, thanks to the fine-grained, note-by-noterepresentation. Moreover, our experiments suggest that graph convolution canlearn non-local features that assist in cadence detection, freeing us from theneed of having to devise specialized features that encode non-local context. Weargue that this general approach to modeling musical scores and classificationtasks has a number of potential advantages, beyond the specific recognitiontask presented here.

Cadence Detection in Symbolic Classical Music using Graph Neural Networks