# Sheet Music Benchmark: Standardized Optical Music Recognition Evaluation

arXiv: https://arxiv.org/abs/2506.10488 · HTML: https://mlsanity.com/paper/2506.10488/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 12 Jun 2025.

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

- [Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders](https://mlsanity.com/paper/1704.01279/) (2017)
- [Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset](https://mlsanity.com/paper/1810.12247/) (2018)
- [Understanding Optical Music Recognition](https://mlsanity.com/paper/1908.03608/) (2019)
- [Sheet Music Transformer: End-To-End Optical Music Recognition Beyond Monophonic Transcription](https://mlsanity.com/paper/2402.07596/) (2024)
- [Partitura: A Python Package for Symbolic Music Processing](https://mlsanity.com/paper/2206.01071/) (2022)
- [End-to-End Full-Page Optical Music Recognition for Pianoform Sheet Music](https://mlsanity.com/paper/2405.12105/) (2024)
- [Optical Music Recognition: State of the Art and Major Challenges](https://mlsanity.com/paper/2006.07885/) (2020)

## Built on by (most cited first, 8 of 8)

- [LEGATO: Large-scale End-to-end Generalizable Approach to Typeset OMR](https://mlsanity.com/paper/2506.19065/) (2025)
- [Transcoda: End-to-End Zero-Shot Optical Music Recognition via Data-Centric Synthetic Training](https://mlsanity.com/paper/2605.10835/) (2026)
- [Optical Music Recognition for Real-World Manuscripts with Synthetic Data](https://mlsanity.com/paper/2606.09479/) (2026)
- [A High-Accuracy Optical Music Recognition Method Based on Bottleneck Residual Convolutions](https://mlsanity.com/paper/2604.16446/) (2026)
- [A Dataset for the Recognition of Historical and Handwritten Music Scores in Western Notation](https://mlsanity.com/paper/2605.18436/) (2026)
- [Rubato: Transcribing Piano Music with Timestamps](https://mlsanity.com/paper/2605.24291/) (2026)
- [LEGATO 2: Toward Multimodal Sheet Music Recognition and Understanding](https://mlsanity.com/paper/2607.05769/) (2026)
- [A Dataset and Benchmark for Optical Music Recognition of String Quartet Scores](https://mlsanity.com/paper/2608.10978/) (2026)

## Abstract (arXiv)

In this work, we introduce the Sheet Music Benchmark (SMB), a dataset of six hundred and eighty-five pages specifically designed to benchmark Optical Music Recognition (OMR) research. SMB encompasses a diverse array of musical textures, including monophony, pianoform, quartet, and others, all encoded in Common Western Modern Notation using the Humdrum **kern format. Alongside SMB, we introduce the OMR Normalized Edit Distance (OMR-NED), a new metric tailored explicitly for evaluating OMR performance. OMR-NED builds upon the widely-used Symbol Error Rate (SER), offering a fine-grained and detailed error analysis that covers individual musical elements such as note heads, beams, pitches, accidentals, and other critical notation features. The resulting numeric score provided by OMR-NED facilitates clear comparisons, enabling researchers and end-users alike to identify optimal OMR approaches. Our work thus addresses a long-standing gap in OMR evaluation, and we support our contributions with baseline experiments using standardized SMB dataset splits for training and assessing state-of-the-art methods.