{"ID":2867202,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.19412","arxiv_id":"2509.19412","title":"EngravingGNN: A Hybrid Graph Neural Network for End-to-End Piano Score Engraving","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.","short_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 coll...","url_abs":"https://arxiv.org/abs/2509.19412","url_pdf":"https://arxiv.org/pdf/2509.19412v1","authors":"[\"Emmanouil Karystinaios\",\"Francesco Foscarin\",\"Gerhard Widmer\"]","published":"2025-09-23T14:48:35Z","proceeding":"cs.GR","tasks":"[\"cs.GR\",\"cs.AI\"]","methods":"[\"Graph Neural Network\"]","has_code":false}