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

TL;DR

This paper introduces a Graph Neural Network-based method for automatic Roman Numeral analysis that directly processes note-wise features and achieves higher accuracy than existing models.

Contribution

It presents a novel GNN architecture with an edge contraction algorithm for onset-wise Roman Numeral prediction from note features.

Findings

ChordGNN outperforms state-of-the-art models in accuracy

The model effectively leverages note interdependencies

Post-processing techniques further improve predictions

Abstract

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