Roman Numeral Analysis with Graph Neural Networks: Onset-wise Predictions from Note-wise Features
This addresses the problem of chord identification in tonal music for music analysis and generation, representing an incremental improvement over existing techniques.
The paper tackles automatic Roman Numeral analysis in symbolic music by proposing a Graph Neural Network (GNN) method that processes individual notes directly, outperforming state-of-the-art models with higher accuracy on reference datasets.
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