EngravingGNN: A Hybrid Graph Neural Network for End-to-End Piano Score Engraving

• URL: http://arxiv.org/abs/2509.19412v1
• Date: Tue, 23 Sep 2025 14:48:35 GMT
• Title: EngravingGNN: A Hybrid Graph Neural Network for End-to-End Piano Score Engraving
• Authors: Emmanouil Karystinaios, Francesco Foscarin, Gerhard Widmer,
• Abstract summary: We propose a unified graph neural network (GNN) framework that targets the case of piano music and quantized symbolic input.<n>Our method employs a multi-task GNN to jointly predict voice connections, staff assignments, pitch spelling, key signature, stem direction, octave shifts, and signs.<n>A dedicated postprocessing pipeline generates print-ready MusicXML/MEI outputs.
• Score: 11.904295041273201
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper focuses on automatic music engraving, i.e., the creation of a humanly-readable musical score from musical content. This step is fundamental for all applications that include a human player, but it remains a mostly unexplored topic in symbolic music processing. In this work, we formalize the problem as a collection of interdependent subtasks, and propose a unified graph neural network (GNN) framework that targets the case of piano music and quantized symbolic input. Our method employs a multi-task GNN to jointly predict voice connections, staff assignments, pitch spelling, key signature, stem direction, octave shifts, and clef signs. A dedicated postprocessing pipeline generates print-ready MusicXML/MEI outputs. Comprehensive evaluation on two diverse piano corpora (J-Pop and DCML Romantic) demonstrates that our unified model achieves good accuracy across all subtasks, compared to existing systems that only specialize in specific subtasks. These results indicate that a shared GNN encoder with lightweight task-specific decoders in a multi-task setting offers a scalable and effective solution for automatic music engraving.

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