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Roman Numeral analysis is the important task of identifying chords and theirfunctional context in pieces of tonal music. This paper presents a new approachto automatic Roman Numeral analysis in symbolic music. While existingtechniques rely on an intermediate lossy representation of the score, wepropose a new method based on Graph Neural Networks (GNNs) that enable thedirect description and processing of each individual note in the score. Theproposed architecture can leverage notewise features and interdependenciesbetween notes but yield onset-wise representation by virtue of our novel edgecontraction algorithm. Our results demonstrate that ChordGNN outperformsexisting state-of-the-art models, achieving higher accuracy in Roman Numeralanalysis on the reference datasets. In addition, we investigate variants of ourmodel using proposed techniques such as NADE, and post-processing of the chordpredictions. The full source code for this work is available athttps://github.com/manoskary/chordgnn

Roman Numeral Analysis with Graph Neural Networks: Onset-wise Predictions from Note-wise Features