Sheet Music Benchmark: Standardized Optical Music Recognition Evaluation
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918057708879872 |
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| author | Martinez-Sevilla, Juan C. Cerveto-Serrano, Joan Luna, Noelia Chapman, Greg Sapp, Craig Rizo, David Calvo-Zaragoza, Jorge |
| author_facet | Martinez-Sevilla, Juan C. Cerveto-Serrano, Joan Luna, Noelia Chapman, Greg Sapp, Craig Rizo, David Calvo-Zaragoza, Jorge |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10488 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sheet Music Benchmark: Standardized Optical Music Recognition Evaluation Martinez-Sevilla, Juan C. Cerveto-Serrano, Joan Luna, Noelia Chapman, Greg Sapp, Craig Rizo, David Calvo-Zaragoza, Jorge Computer Vision and Pattern Recognition Digital Libraries Information Retrieval 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. |
| title | Sheet Music Benchmark: Standardized Optical Music Recognition Evaluation |
| topic | Computer Vision and Pattern Recognition Digital Libraries Information Retrieval |
| url | https://arxiv.org/abs/2506.10488 |