Understanding Optical Music Recognition
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http://hdl.handle.net/10045/108236
Título: | Understanding Optical Music Recognition |
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Autor/es: | Calvo-Zaragoza, Jorge | Hajič Jr., Jan | Pacha, Alexander |
Grupo/s de investigación o GITE: | Reconocimiento de Formas e Inteligencia Artificial |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | Optical Music Recognition | Music Notation | Music Scores |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | jul-2020 |
Editor: | Association for Computing Machinery (ACM) |
Cita bibliográfica: | ACM Computing Surveys. 2020, 53(4): Article 77. https://doi.org/10.1145/3397499 |
Resumen: | 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. |
URI: | http://hdl.handle.net/10045/108236 |
ISSN: | 0360-0300 (Print) | 1557-7341 (Online) |
DOI: | 10.1145/3397499 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1145/3397499 |
Aparece en las colecciones: | INV - GRFIA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Calvo-Zaragoza_etal_ACMComputSurv_final.pdf | Versión final (acceso restringido) | 3,82 MB | Adobe PDF | Abrir Solicitar una copia |
Calvo-Zaragoza_etal_ACMComputSurv_preprint.pdf | Preprint (acceso abierto) | 2,19 MB | Adobe PDF | Abrir Vista previa |
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