One-Step Detection of Background, Staff Lines, and Symbols in Medieval Music Manuscripts with Convolutional Neural Networks

Autor: Jorge Calvo-Zaragoza, Vigliensoni, G., Fujinaga, I.

Publikováno v: Scopus-Elsevier

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Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4fee6f3cff12275f59ea3c93c99f7c7d

MusicBrainz for the World: The Chilean Experience

Autor: Vigliensoni, G., Burgoyne, J.A., Fujinaga, I., de Souza Britto Jr., A., Gouyon, F., Dixon, S.

Publikováno v: Proceedings of the 14th International Society for Music Information Retrieval Conference: November 4-8, 2013, Curitiba, Brazil, 131-136
STARTPAGE=131;ENDPAGE=136;TITLE=Proceedings of the 14th International Society for Music Information Retrieval Conference

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::635b9fa5eb140648e37d292529d32361
https://dare.uva.nl/personal/pure/en/publications/musicbrainz-for-the-world-the-chilean-experience(08f685d3-eef4-4a01-8e02-a3a6884606c9).html

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Document analysis of music score images with selectional auto-encoders

Autor: Castellanos, F. J., Jorge Calvo-Zaragoza, Vigliensoni, G., Fujinaga, I.

Publikováno v: Scopus-Elsevier

The document analysis of music score images is a key step in the development of successful Optical Music Recognition systems. The current state of the art considers the use of deep neural networks trained to classify every pixel of the image accordin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a93a24da76afc77fee16e9eb29e463a
http://www.scopus.com/inward/record.url?eid=2-s2.0-85069842280&partnerID=MN8TOARS

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