Optical music recognition and manuscript chant sources

Helsen, Kate; Bain, Jennifer; Fujinaga, Ichiro; Hankinson, Andrew; Lacoste, Debra

doi:10.1093/em/cau092

Cited by 4 publications

(1 citation statement)

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“…A direct approach for search within music collections is to use OMR technology to transform the documents into symbolic pieces of information, over which classical content-based or symbolic retrieval methods can be used [87], [22], [104], [287], [157], [173], [1], [97]. The problem is that these transformations require a more comprehensive understanding of the processed documents (see Sections VI-B3 and VI-B4 below).…”

Section: Definition 2 Document Metadata Extraction Refers To a Class ...mentioning

confidence: 99%

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: Definition 2 Document Metadata Extraction Refers To a Class ...mentioning

confidence: 99%

Understanding Optical Music Recognition

Calvo-Zaragoza,

Hajič,

Pacha

2019

Preprint

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Understanding Optical Music Recognition

2020

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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 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.

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Connecting online early music libraries and musicological resources: Experiments in ergonomics in the Biblissima+ framework

Fiala,

Roger

2023

Proceedings of the 10th International Conference on Digital Libraries for Musicology

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Optical music recognition and manuscript chant sources

Cited by 4 publications

References 0 publications

Understanding Optical Music Recognition

Understanding Optical Music Recognition

Understanding Optical Music Recognition

Connecting online early music libraries and musicological resources: Experiments in ergonomics in the Biblissima+ framework

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