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

arXiv: https://arxiv.org/abs/2509.19412 · HTML: https://mlsanity.com/paper/2509.19412/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [Partitura: A Python Package for Symbolic Music Processing](https://mlsanity.com/paper/2206.01071/) (2022)
- [Non-Local Musical Statistics as Guides for Audio-to-Score Piano Transcription](https://mlsanity.com/paper/2008.12710/) (2020)
- [End-to-end Piano Performance-MIDI to Score Conversion with Transformers](https://mlsanity.com/paper/2410.00210/) (2024)
- [PKSpell: Data-Driven Pitch Spelling and Key Signature Estimation](https://mlsanity.com/paper/2107.14009/) (2021)
- [GraphMuse: A Library for Symbolic Music Graph Processing](https://mlsanity.com/paper/2407.12671/) (2024)
- [Engraving Oriented Joint Estimation of Pitch Spelling and Local and Global Keys](https://mlsanity.com/paper/2402.10247/) (2024)
- [Cluster and Separate: a GNN Approach to Voice and Staff Prediction for Score Engraving](https://mlsanity.com/paper/2407.21030/) (2024)

## Abstract (arXiv)

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.