An Online Tool for Semi-Automatically Annotating Music Scores for Optical Music Recognition-Reference-Cited by-同舟云学术

An Online Tool for Semi-Automatically Annotating Music Scores for Optical Music Recognition

Published:2024-06-27 Issue: Volume:25 Page:73-77
ISSN:
Container-title:Proceedings of the 11th International Conference on Digital Libraries for Musicology
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Author:

Graczyk Stanisław¹^ORCID,Piniarska Zuzanna¹^ORCID,Kałamoniak Mateusz¹^ORCID,Łukaszewski Tomasz¹^ORCID,Łukasik Ewa¹^ORCID

Affiliation:

1. Institute of Computing Science, Poznan University of Technology, Poland

Funder

Smart Growth Operational Programme 2014-2020 in the area ?Development of modern research infrastructure of the science sector?

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3660570.3660571

Reference20 articles.

1. Mobile System for Optical Music Recognition and Music Sound Generation

2. Understanding Optical Music Recognition

3. Jorge Calvo-Zaragoza and David Rizo. 2018. Camera-PrIMuS: Neural End-to-End Optical Music Recognition on Realistic Monophonic Scores. In Proceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018, Paris, France, September 23-27, 2018, Emilia Gómez, Xiao Hu, Eric Humphrey, and Emmanouil Benetos (Eds.). 248–255. http://ismir2018.ircam.fr/doc/pdfs/33_Paper.pdf

4. Handwritten Music Recognition for Mensural notation with convolutional recurrent neural networks

5. Francisco J. Castellanos, Jorge Calvo-Zaragoza, and José M. Iñesta. 2020. A Neural Approach for Full-Page Optical Music Recognition of Mensural Documents. In Proceedings of the 21th International Society for Music Information Retrieval Conference, ISMIR 2020, Montreal, Canada, October 11-16, 2020, Julie Cumming, Jin Ha Lee, Brian McFee, Markus Schedl, Johanna Devaney, Cory McKay, Eva Zangerle, and Timothy de Reuse (Eds.). 558–565. http://archives.ismir.net/ismir2020/paper/000207.pdf