How to Exploit Music Notation Syntax for OMR?

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10372149" target="_blank" >RIV/00216208:11320/17:10372149 - isvavai.cz</a>

Výsledek na webu

<a href="http://doi.org/10.1109/ICDAR.2017.275" target="_blank" >http://doi.org/10.1109/ICDAR.2017.275</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICDAR.2017.275" target="_blank" >10.1109/ICDAR.2017.275</a>

Jazyk výsledku

angličtina

Název v původním jazyce

How to Exploit Music Notation Syntax for OMR?

Popis výsledku v původním jazyce

A major roadblock for Optical Music Recognition, especially for handwritten music notation, is symbol detection: recovering the locations of musical symbols from the input page. This has been attempted both with bottom-up approaches exploiting visual features, and top-down approaches based on the strong constraints that music notation syntax imposes on possible symbol configurations; sometimes joined together at appropriate points in the recognition process. The bottom-up approach has recently greatly improved with the boom of neural networks. However, the reduction in uncertainty that music notation syntax can provide has not yet been married to the power of these neural network models. This extended abstract brainstorms ways in which this can be done, and analyzes the difficulties the various combined approaches will have to address. We hope our work will foster further discussion to clarify the issues involed, provoke OMR researchers to try some of these approaches experimentally, and entice resear

Název v anglickém jazyce

How to Exploit Music Notation Syntax for OMR?

Popis výsledku anglicky

A major roadblock for Optical Music Recognition, especially for handwritten music notation, is symbol detection: recovering the locations of musical symbols from the input page. This has been attempted both with bottom-up approaches exploiting visual features, and top-down approaches based on the strong constraints that music notation syntax imposes on possible symbol configurations; sometimes joined together at appropriate points in the recognition process. The bottom-up approach has recently greatly improved with the boom of neural networks. However, the reduction in uncertainty that music notation syntax can provide has not yet been married to the power of these neural network models. This extended abstract brainstorms ways in which this can be done, and analyzes the difficulties the various combined approaches will have to address. We hope our work will foster further discussion to clarify the issues involed, provoke OMR researchers to try some of these approaches experimentally, and entice resear

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

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 12th IAPR International Workshop on Graphics Recognition

ISBN

978-1-5386-3586-5

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

2

Strana od-do

55-56

Název nakladatele

IEEE Computer Society

Místo vydání

New York, USA

Místo konání akce

Kyoto, Japan

Datum konání akce

9. 11. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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