Cluster and Separate: a GNN Approach to Voice and Staff Prediction for Score Engraving
arxiv(2024)
摘要
This paper approaches the problem of separating the notes from a quantized
symbolic music piece (e.g., a MIDI file) into multiple voices and staves. This
is a fundamental part of the larger task of music score engraving (or score
typesetting), which aims to produce readable musical scores for human
performers. We focus on piano music and support homophonic voices, i.e., voices
that can contain chords, and cross-staff voices, which are notably difficult
tasks that have often been overlooked in previous research. We propose an
end-to-end system based on graph neural networks that clusters notes that
belong to the same chord and connects them with edges if they are part of a
voice. Our results show clear and consistent improvements over a previous
approach on two datasets of different styles. To aid the qualitative analysis
of our results, we support the export in symbolic music formats and provide a
direct visualization of our outputs graph over the musical score. All code and
pre-trained models are available at https://github.com/CPJKU/piano_svsep
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