Contributions to Proceedings:

L. Tuggener, Y. Satyawan, A. Pacha, J. Schmidhuber, T. Stadelmann:
"The DeepScoresV2 Dataset and Benchmark for Music Object Detection";
in: "Proceedings of the 25th International Conference on Pattern Recognition 2020", 20; issued by: IAPR; Springer International Publishing, Milan, Italy, 2020, ISBN: 978-1-7281-8808-9, 9188 - 9195.

English abstract:
In this paper, we present DeepScoresV2, an extended version of the DeepScores dataset for optical music recognition (OMR). We improve upon the original DeepScores dataset by providing much more detailed annotations, namely (a) annotations for 135 classes including fundamental symbols of non-fixed size and shape, increasing the number of annotated symbols by 23%; (b) oriented bounding boxes; (c) higher-level rhythm and pitch information (onset beat for all symbols and line position for noteheads); and (d) a compatibility mode for easy use in conjunction with the MUSCIMA++ dataset for OMR on handwritten documents. These additions open up the potential for future advancement in OMR research. Additionally, we release two state-of-the-art baselines for DeepScoresV2 based on Faster R-CNN and the Deep Watershed Detector. An analysis of the baselines shows that regular orthogonal bounding boxes are unsuitable for objects which are long, small, and potentially rotated, such as ties and beams, which demonstrates the need for detection algorithms that naturally incorporate object angles.

Optical music recognition; Deep neural net; Music object detection; Object detection; Computer vision; Pattern recognition

"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.