Sheet Music Benchmark: Standardized Optical Music Recognition Evaluation
Juan C. Martinez-Sevilla, Joan Cerveto-Serrano, Noelia Luna, Greg Chapman, Craig Sapp, David Rizo, Jorge Calvo-Zaragoza

TL;DR
This paper introduces the Sheet Music Benchmark (SMB), a comprehensive dataset for evaluating Optical Music Recognition (OMR), along with a new evaluation metric, OMR-NED, to improve accuracy and comparability in OMR research.
Contribution
The paper presents the SMB dataset and the OMR-NED metric, providing standardized tools for benchmarking and detailed error analysis in OMR research.
Findings
Baseline experiments demonstrate the effectiveness of SMB and OMR-NED.
OMR-NED offers more detailed error insights than traditional metrics.
SMB covers diverse musical textures for comprehensive evaluation.
Abstract
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…
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Taxonomy
TopicsMusic and Audio Processing · Music Technology and Sound Studies · Speech and Audio Processing
