Talks and Poster Presentations (with Proceedings-Entry):

A. Pacha, H. Eidenberger:
"Towards a Universal Music Symbol Classifier";
Talk: International Workshop on Graphics Recognition, New York; 2017-11-09 - 2017-11-10; in: "Proceedings of the 12th International Workshop on Graphics Recognition", (2017), ISBN: 978-1-5386-3586-5; 2 pages.

English abstract:
Optical Music Recognition (OMR) aims to recognize and
understand written
music scores. With the help of Deep Learning, researchers were able
to significantly improve the state-of-the-art in this research area.
However, Deep Learning requires a substantial amount of annotated
data for supervised training. Various datasets have been collected
in the past, but without a common standard that defines data formats
and terminology, combining them is a challenging task. In this paper
we present our approach towards unifying multiple datasets into the
largest currently available body of over 90000 musical symbols that
belong to 79 classes, containing both handwritten and printed music
symbols. A universal music symbol classifier, trained on such a dataset
using Deep Learning, can achieve an accuracy that exceeds 98%.

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.