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Publikationstyp: Konferenz: Paper
Art der Begutachtung: Peer review (Publikation)
Titel: The DeepScoresV2 dataset and benchmark for music object detection
Autor/-in: Tuggener, Lukas
Satyawan, Yvan Putra
Pacha, Alexander
Schmidhuber, Jürgen
Stadelmann, Thilo
et. al: No
DOI: 10.1109/ICPR48806.2021.9412290
10.21256/zhaw-20647
Tagungsband: 2020 25th International Conference on Pattern Recognition (ICPR)
Seite(n): 9188
Seiten bis: 9195
Angaben zur Konferenz: 25th International Conference on Pattern Recognition 2020 (ICPR’20), Online, 10-15 January 2021
Erscheinungsdatum: 2021
Verlag / Hrsg. Institution: IEEE
ISBN: 978-1-7281-8808-9
Sprache: Englisch
Schlagwörter: Optical music recognition; Deep neural net; Music object detection; Object detection; Computer vision; Pattern recognition
Fachgebiet (DDC): 006: Spezielle Computerverfahren
Zusammenfassung: 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.
Weitere Angaben: The dataset, code and pre-trained models, as well as user instructions, are publicly available at https://zenodo.org/record/4012193.
URI: https://digitalcollection.zhaw.ch/handle/11475/20647
Zugehörige Forschungsdaten: https://zenodo.org/record/4012193
Volltext Version: Akzeptierte Version
Lizenz (gemäss Verlagsvertrag): Lizenz gemäss Verlagsvertrag
Departement: School of Engineering
Organisationseinheit: Institut für Informatik (InIT)
Publiziert im Rahmen des ZHAW-Projekts: RealScore - Scanning of Real-World Sheet Music for a Digital Music Stand
Enthalten in den Sammlungen:Publikationen School of Engineering

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Tuggener, L., Satyawan, Y. P., Pacha, A., Schmidhuber, J., & Stadelmann, T. (2021). The DeepScoresV2 dataset and benchmark for music object detection [Conference paper]. 2020 25th International Conference on Pattern Recognition (ICPR), 9188–9195. https://doi.org/10.1109/ICPR48806.2021.9412290
Tuggener, L. et al. (2021) ‘The DeepScoresV2 dataset and benchmark for music object detection’, in 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, pp. 9188–9195. Available at: https://doi.org/10.1109/ICPR48806.2021.9412290.
L. Tuggener, Y. P. Satyawan, A. Pacha, J. Schmidhuber, and T. Stadelmann, “The DeepScoresV2 dataset and benchmark for music object detection,” in 2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 9188–9195. doi: 10.1109/ICPR48806.2021.9412290.
TUGGENER, Lukas, Yvan Putra SATYAWAN, Alexander PACHA, Jürgen SCHMIDHUBER und Thilo STADELMANN, 2021. The DeepScoresV2 dataset and benchmark for music object detection. In: 2020 25th International Conference on Pattern Recognition (ICPR). Conference paper. IEEE. 2021. S. 9188–9195. ISBN 978-1-7281-8808-9
Tuggener, Lukas, Yvan Putra Satyawan, Alexander Pacha, Jürgen Schmidhuber, and Thilo Stadelmann. 2021. “The DeepScoresV2 Dataset and Benchmark for Music Object Detection.” Conference paper. In 2020 25th International Conference on Pattern Recognition (ICPR), 9188–95. IEEE. https://doi.org/10.1109/ICPR48806.2021.9412290.
Tuggener, Lukas, et al. “The DeepScoresV2 Dataset and Benchmark for Music Object Detection.” 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, 2021, pp. 9188–95, https://doi.org/10.1109/ICPR48806.2021.9412290.


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