构建首个手写爵士乐谱数据集并开发专用识别模型。
Optical Music Recognition of Jazz Lead Sheets
- 设计针对爵士乐谱的分词策略，融合真实与合成图像训练。
- 涵盖293张手写谱面、2021个五线谱，含标准格式标注。
- 适合音乐信息检索、自动伴奏生成等研究者使用。
本文针对手写爵士乐谱的光学乐谱识别（OMR）挑战展开研究，此类乐谱广泛记录旋律与和弦，但现有系统缺乏对和弦成分的处理能力，且手写图像存在高度变异与质量参差问题。本工作贡献包含两方面：首先构建了一个全新数据集，包含293张手写爵士乐谱，覆盖163首独特曲目，共2021个五线谱，均配有基于Humdrum kern和MusicXML的标准标注；同时提供由真实标注生成的合成图像。其次，提出一种面向爵士乐谱的OMR模型，探讨了适配该类数据的分词方法，并验证了合成数据与预训练模型的优势。所有代码、数据与模型均已公开发布。
原文摘要 · Abstract (English)
In this paper, we address the challenge of Optical Music Recognition (OMR) for handwritten jazz lead sheets, a widely used musical score type that encodes melody and chords. The task is challenging due to the presence of chords, a score component not handled by existing OMR systems, and the high variability and quality issues associated with handwritten images. Our contribution is two-fold. We present a novel dataset consisting of 293 handwritten jazz lead sheets of 163 unique pieces, amounting to 2021 total staves aligned with Humdrum **kern and MusicXML ground truth scores. We also supply synthetic score images generated from the ground truth. The second contribution is the development of an OMR model for jazz lead sheets. We discuss specific tokenisation choices related to our kind of data, and the advantages of using synthetic scores and pretrained models. We publicly release all code, data, and models.
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