Talks and Poster Presentations (with Proceedings-Entry):

A. Pacha, H. Eidenberger:
"Towards Self-Learning Optical Music Recognition";
Talk: 16th IEEE International Conference on Machine Learning and Applications, Cancun; 2017-12-18 - 2017-12-21; in: "2017 16th IEEE International Conference on Machine Learning and Applications", (2017), 6 pages.

English abstract:
Optical Music Recognition (OMR) is a branch of
artificial intelligence that aims at automatically recognizing
and understanding the content of music scores in images.
Several approaches and systems have been proposed that try to
solve this problem by using expert knowledge and specialized
algorithms that tend to fail at generalization to a broader
set of scores, imperfect image scans or data of different
formatting. In this paper we propose a new approach to solve
OMR by investigating how humans read music scores and by
imitating that behavior with machine learning. To demonstrate
the power of this approach, we conduct two experiments
that teach a machine to distinguish entire music sheets from
arbitrary content through frame-by-frame classification and
distinguishing between 32 classes of handwritten music symbols
which can be a basis for object detection. Both tasks can
be performed at high rates of confidence (>98%) which is
comparable to the performance of humans on the same task.

"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.