  Roman Numeral analysis is the important task of identifying chords and their
functional context in pieces of tonal music. This paper presents a new approach
to automatic Roman Numeral analysis in symbolic music. While existing
techniques rely on an intermediate lossy representation of the score, we
propose a new method based on Graph Neural Networks (GNNs) that enable the
direct description and processing of each individual note in the score. The
proposed architecture can leverage notewise features and interdependencies
between notes but yield onset-wise representation by virtue of our novel edge
contraction algorithm. Our results demonstrate that ChordGNN outperforms
existing state-of-the-art models, achieving higher accuracy in Roman Numeral
analysis on the reference datasets. In addition, we investigate variants of our
model using proposed techniques such as NADE, and post-processing of the chord
predictions. The full source code for this work is available at
https://github.com/manoskary/chordgnn
