Cluster and Separate: a GNN Approach to Voice and Staff Prediction for Score Engraving

Autor:	Foscarin, Francesco, Karystinaios, Emmanouil, Nakamura, Eita, Widmer, Gerhard
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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 Comment: Accepted at the 25th International Society for Music Information Retrieval (ISMIR) 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.21030 Zobrazit plný text záznamu View this record from Arxiv