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

2025-09-23

AI Summary

This work addresses the problem of automatic piano score layout. We propose the first graph neural network (GNN)-based multi-task joint modeling framework for this task. The musical score structure is formalized as a heterogeneous music graph, enabling unified modeling of core subtasks—including voice connectivity, staff assignment, pitch spelling, key signature, stem direction, octave indication, and clef assignment. Our architecture employs a shared GNN encoder and lightweight task-specific decoders to achieve end-to-end, style-agnostic generation of printable MusicXML/MEI output. Unlike conventional pipeline-based or single-task systems, our approach eliminates task fragmentation, thereby enhancing generalization and system integration. Evaluated on two piano datasets—J-Pop and DCML Romantic—our method achieves state-of-the-art accuracy across all subtasks, demonstrating both effectiveness and robustness.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Automating creation of readable piano scores from symbolic music content
• Solving interdependent engraving subtasks using unified graph neural network
• Generating print-ready MusicXML/MEI outputs for piano music engraving

Innovation

• Multi-task GNN for joint engraving subtasks
• Lightweight task-specific decoders architecture
• Postprocessing pipeline generates MusicXML/MEI outputs