Discussion Group Summary: Optical Music Recognition

Calvo-Zaragoza, Jorge; Hajič, Jan; Pacha, Alexander

doi:10.1007/978-3-030-02284-6_12

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…Our motivation is to find natural groups of OMR applications and tasks for which we can expect, among other things, shared evaluation protocols. The need for such systematization has long been felt [34], [49], but subsequent reviews [247], [211] have focused almost entirely on technical solutions.…”

Section: A Taxonomy Of Omrmentioning

confidence: 99%

“…An extremely complex system, on the other hand, would allow images (offline) of handwritten music in common western notation from degraded documents as input and strive to recognize the full structured encoding in an end-to-end system. met in person [49]. This unstable setting and researchers that were not paying sufficient attention to reproducibility led to the same problems being solved over and over again [215].…”

Section: A Open Issues and Perspectives For Future Researchmentioning

confidence: 99%

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Understanding Optical Music Recognition

Calvo-Zaragoza,

Hajič,

Pacha

2019

Preprint

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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 tutorial, 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, (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.

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Section: A Taxonomy Of Omrmentioning

confidence: 99%

Section: A Open Issues and Perspectives For Future Researchmentioning

confidence: 99%

Understanding Optical Music Recognition

Calvo-Zaragoza,

Hajič,

Pacha

2019

Preprint

Self Cite

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“…Among those, Western classical music has been one of the earliest focus areas (Hewlett and Selfridge-Field, 1991), which-from a technical perspective-provides interesting opportunities because of its multi-modal resources: there is a specific correspondence between what is written down in sheet music (either represented as graphical or symbolic data) and what is recorded (audio data)-typically comprising a multitude of professional performances. This not only allows for studying performance aspects (Lerch et al, 2020) but also for developing and testing algorithmic approaches to various tasks including automatic music transcription (Benetos et al, 2019), optical music recognition (Calvo-Zaragoza et al, 2020), music synchronization (Müller et al, 2021), retrieval (Müller et al, 2019), and analysis (Nieto et al, 2020;Bosch, 2013;Meredith, 2016).…”

Section: Introductionmentioning

confidence: 99%

Wagner Ring Dataset: A Complex Opera Scenario for Music Processing and Computational Musicology

Weiß,

Arifi-Müller,

Krause

et al. 2023

Transactions of the International Society for Music Information Retrieval

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This paper introduces the Wagner Ring Dataset (WRD), a multi-modal and multiversion resource on the large-scale opera cycle Der Ring des Nibelungen by Richard Wagner. The Ring comprises four music dramas organized into eleven acts and 21 939 measures in total. Concerning sheet music, we processed a publicly available piano reduction (822 pages) of the full score with optical music recognition followed by extensive manual corrections to create a high-quality, machine-readable symbolic score. Concerning audio data, our corpus covers 16 recorded performances of the full Ring (three of which are publicly available thanks to copyright expiry), each lasting about 14-15 hours. To musically synchronize these versions among each other, we manually annotated all measure positions for three performances, which we transferred to the remaining performances via automated synchronization techniques. The dataset further comprises annotations of key and time signatures, scenes, and singing voice regions (libretto). Moreover, we provide note event annotations for all performances derived from the piano score. The WRD thus constitutes a comprehensive resource for developing algorithms for various music information retrieval tasks, complementing existing datasets with a complex opera scenario. For computational musicology, the WRD serves as a structured dataset that allows for studying the composition and performances of the Ring.

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Discussion Group Summary: Optical Music Recognition

Cited by 2 publications

References 14 publications

Understanding Optical Music Recognition

Understanding Optical Music Recognition

Wagner Ring Dataset: A Complex Opera Scenario for Music Processing and Computational Musicology

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