首个标准化乐谱识别评测数据集，解决长期缺乏统一评估标准的问题。
Sheet Music Benchmark: Standardized Optical Music Recognition Evaluation
- 构建685页多样乐谱数据集，覆盖多种音乐织体与记谱规范。
- 提出新度量指标OMR-NED，精细分析音符、附点、调号等各类错误。
- 提供标准训练/测试划分，助力模型对比与性能提升。
本文提出Sheet Music Benchmark（SMB），一个包含685页乐谱的标准化数据集，专门用于评估光学乐谱识别（OMR）技术。SMB涵盖单声部、钢琴谱、四重奏等多种音乐织体，均以通用西方现代记谱法表示，并采用Humdrum的kern格式编码。同时，我们引入了专为OMR设计的评价指标——OMR归一化编辑距离（OMR-NED），该指标在广泛使用的符号错误率（SER）基础上，细化到对音符头、符干、音高、调号等关键记谱元素的逐项分析，提供更精确的错误定位。通过标准化的训练与测试划分，我们进行了基线实验，验证了SMB与OMR-NED在推动当前主流方法比较与优化方面的有效性，填补了长期存在的评估体系空白。
原文摘要 · Abstract (English)
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.
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