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

翻译：罗马数字分析是识别调性音乐作品中和弦及其功能背景的重要任务。本文提出了一种新的符号音乐自动罗马数字分析方法。现有技术依赖于乐谱的中间有损表示，而我们提出了一种基于图神经网络（GNN）的新方法，能够直接在乐谱中描述和处理每个音符。所提出的架构可利用音符级特征及音符间的相互依赖关系，并通过我们新颖的边收缩算法生成节拍级表示。实验结果表明，ChordGNN在参考数据集上的罗马数字分析精度超越了现有最先进模型。此外，我们还研究了采用所提出的NADE技术及和弦预测后处理等方法的模型变体。本研究的完整源代码可从https://github.com/manoskary/chordgnn获取。