Two Journeys: Insights on the Annotation of Large-Scale Optical Music Recognition Datasets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10511665" target="_blank" >RIV/00216208:11320/25:10511665 - isvavai.cz</a>

Výsledek na webu

<a href="https://zenodo.org/records/17498017" target="_blank" >https://zenodo.org/records/17498017</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5281/zenodo.17498017" target="_blank" >10.5281/zenodo.17498017</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Two Journeys: Insights on the Annotation of Large-Scale Optical Music Recognition Datasets

Popis výsledku v původním jazyce

The use of Optical Music Recognition (OMR) requires annotated music scores (ground truth) for training or adaptation. Besides the level of structured encoding, a significant part of the existing OMR methods still needs ground truth also at image level, which is indeed still important for tasks adjacent to OMR. However, annotating images is a major part of OMR project costs: not just the work of the annotators themselves but also associated software development and process management. Unlike most other more prevalent image annotation tasks in industry, in the case of music notation, infrastructure is lacking, and many pitfalls exist. Thus, in this paper, we report on the combined experience of two ongoing OMR projects with major data collection and annotation efforts, and we formulate recommendations for managing music notation annotation projects, leading to efficient use of the inevitably limited resources.

Název v anglickém jazyce

Two Journeys: Insights on the Annotation of Large-Scale Optical Music Recognition Datasets

Popis výsledku anglicky

The use of Optical Music Recognition (OMR) requires annotated music scores (ground truth) for training or adaptation. Besides the level of structured encoding, a significant part of the existing OMR methods still needs ground truth also at image level, which is indeed still important for tasks adjacent to OMR. However, annotating images is a major part of OMR project costs: not just the work of the annotators themselves but also associated software development and process management. Unlike most other more prevalent image annotation tasks in industry, in the case of music notation, infrastructure is lacking, and many pitfalls exist. Thus, in this paper, we report on the combined experience of two ongoing OMR projects with major data collection and annotation efforts, and we formulate recommendations for managing music notation annotation projects, leading to efficient use of the inevitably limited resources.

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

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 17th International Symposium on Computer Music Multidisciplinary Research

ISBN

979-10-97498-06-1

ISSN

—

e-ISSN

—

Počet stran výsledku

15

Strana od-do

608-622

Název nakladatele

The Laboratory PRISM “Perception, Representations, Image, Sound, Music”

Místo vydání

Marseille, France

Místo konání akce

London, UK

Datum konání akce

3. 11. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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