How current optical music recognition systems are becoming useful for digital libraries

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10390148" target="_blank" >RIV/00216208:11320/18:10390148 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11210/18:10390148

Výsledek na webu

<a href="https://dl.acm.org/citation.cfm?id=3273034" target="_blank" >https://dl.acm.org/citation.cfm?id=3273034</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3273024.3273034" target="_blank" >10.1145/3273024.3273034</a>

Jazyk výsledku

angličtina

Název v původním jazyce

How current optical music recognition systems are becoming useful for digital libraries

Popis výsledku v původním jazyce

Optical Music Recognition (OMR) promises to make large collections of sheet music searchable by their musical content. It would open up novel ways of accessing the vast amount of written music that has never been recorded before. For a long time, OMR was not living up to that promise, as its performance was simply not good enough, especially on handwritten music or under non-ideal image conditions. However, OMR has recently seen a number of improvements, mainly due to the advances in machine learning. In this work, we take an OMR system based on the traditional pipeline and an end-to-end system, which represent the current state of the art, and illustrate in proof-of-concept experiments their applicability in retrieval settings. We also provide an example of a musicological study that can be replicated with OMR outputs at much lower costs. Taken together, this indicates that in some settings, current OMR can be used as a general tool for enriching digital libraries.

Název v anglickém jazyce

How current optical music recognition systems are becoming useful for digital libraries

Popis výsledku anglicky

Optical Music Recognition (OMR) promises to make large collections of sheet music searchable by their musical content. It would open up novel ways of accessing the vast amount of written music that has never been recorded before. For a long time, OMR was not living up to that promise, as its performance was simply not good enough, especially on handwritten music or under non-ideal image conditions. However, OMR has recently seen a number of improvements, mainly due to the advances in machine learning. In this work, we take an OMR system based on the traditional pipeline and an end-to-end system, which represent the current state of the art, and illustrate in proof-of-concept experiments their applicability in retrieval settings. We also provide an example of a musicological study that can be replicated with OMR outputs at much lower costs. Taken together, this indicates that in some settings, current OMR can be used as a general tool for enriching digital libraries.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 5th International Conference on Digital Libraries for Musicology

ISBN

978-1-4503-6522-2

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

5

Strana od-do

57-61

Název nakladatele

ACM

Místo vydání

New York, NY, USA

Místo konání akce

Paris, France

Datum konání akce

28. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—