This repo is still under development, it will be finalized shortly
Code for the paper "Cluster and Separate: A GNN Approach to Voice and Staff Prediction for Score Engraving".
This work approaches the problem of separating the notes from a quantised 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 type-setting), 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 visualisation of our outputs graph over the musical score.
To install Piano_SVSep you first need to install the Pytorch version suitable for your system. You can find the instructions here.
You also need to install Pytorch Geometric. You can find the instructions here. We recommend to use conda:
conda install conda install pyg -c pygYou can install the rest of the dependencies using pip:
pip install -r requirements.txt
@inproceedings{piano_SVSep_2024,
title={Cluster and Separate: a GNN Approach to Voice and Staff Prediction for Score Engraving},
author={Foscarin, Francesco and Karystinaios, Emmanouil and Nakamura, Eita and Widmer, Gerhard},
booktitle={Proceedings of the International Society for Music Information Retrieval Conference (ISMIR)},
year={2024}
}