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

arXiv: https://arxiv.org/abs/2307.03544 · HTML: https://mlsanity.com/paper/2307.03544/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 7 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [Multi-Task Learning as a Bargaining Game](https://mlsanity.com/paper/2202.01017/) (2022)
- [Auxiliary Tasks in Multi-task Learning](https://mlsanity.com/paper/1805.06334/) (2018)
- [RotoGrad: Gradient Homogenization in Multitask Learning](https://mlsanity.com/paper/2103.02631/) (2021)
- [Cadence Detection in Symbolic Classical Music using Graph Neural Networks](https://mlsanity.com/paper/2208.14819/) (2022)
- [Musical Voice Separation as Link Prediction: Modeling a Musical Perception Task as a Multi-Trajectory Tracking Problem](https://mlsanity.com/paper/2304.14848/) (2023)
- [Symbolic Music Structure Analysis with Graph Representations and Changepoint Detection Methods](https://mlsanity.com/paper/2303.13881/) (2023)

## Built on by (most cited first, 7 of 7)

- [GraphMuse: A Library for Symbolic Music Graph Processing](https://mlsanity.com/paper/2407.12671/) (2024)
- [Emotion-driven Piano Music Generation via Two-stage Disentanglement and Functional Representation](https://mlsanity.com/paper/2407.20955/) (2024)
- [AnalysisGNN: Unified Music Analysis with Graph Neural Networks](https://mlsanity.com/paper/2509.06654/) (2025)
- [Perception-Inspired Graph Convolution for Music Understanding Tasks](https://mlsanity.com/paper/2405.09224/) (2024)
- [Improving BERT for Symbolic Music Understanding Using Token Denoising and Pianoroll Prediction](https://mlsanity.com/paper/2507.04776/) (2025)
- [BACHI: Boundary-Aware Symbolic Chord Recognition Through Masked Iterative Decoding on Pop and Classical Music](https://mlsanity.com/paper/2510.06528/) (2025)
- [From Prediction to Collaboration: Interactive Symbolic Music Analysis](https://mlsanity.com/paper/2607.13587/) (2026)

## Abstract (arXiv)

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