For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). However, this field is still difficult to access for new researchers, especially those without a significant musical background: Few introductory materials are available, and, furthermore, the field has struggled with defining itself and building a shared terminology. In this work, we address these shortcomings by (1) providing a robust definition of OMR and its relationship to related fields, (2) analyzing how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, and (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. Based on this work, the reader should be able to attain a basic understanding of OMR: its objectives, its inherent structure, its relationship to other fields, the state of the art, and the research opportunities it affords.

中文翻译：

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了解光学音乐识别

50 多年来，研究人员一直在尝试教计算机阅读乐谱，称为光学音乐识别 (OMR)。然而，对于新的研究人员来说，这个领域仍然很难进入，尤其是那些没有重要音乐背景的研究人员：几乎没有可用的介绍性材料，此外，该领域一直在努力定义自己和建立一个共享的术语。在这项工作中，我们通过以下方式解决这些缺点：（1）提供 OMR 的稳健定义及其与相关领域的关系，（2）分析 OMR 如何反转音乐编码过程以从文档中恢复乐谱和音乐语义，以及（ 3) 提出 OMR 的分类法，其中最引人注目的是一种新颖的应用分类法。此外，我们还讨论了深度学习如何影响现代 OMR 研究，相对于传统的管道。基于这项工作，读者应该能够对 OMR 有一个基本的了解：它的目标、它的内在结构、它与其他领域的关系、最先进的技术以及它提供的研究机会。