Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20647
Publication type: Conference paper
Type of review: Peer review (publication)
Title: The DeepScoresV2 dataset and benchmark for music object detection
Authors: Tuggener, Lukas
Satyawan, Yvan Putra
Pacha, Alexander
Schmidhuber, Jürgen
Stadelmann, Thilo
et. al: No
DOI: 10.21256/zhaw-20647
Proceedings: Proceedings of the 25th International Conference on Pattern Recognition 2020 (ICPR’20)
Conference details: 25th International Conference on Pattern Recognition 2020 (ICPR’20), Online, 10-15 January 2021
Issue Date: Oct-2020
Publisher / Ed. Institution: IAPR
Language: English
Subjects: Optical music recognition; Deep neural net; Music object detection; Object detection; Computer vision; Pattern recognition
Subject (DDC): 004: Computer science
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.
Further description: 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
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Published as part of the ZHAW project: RealScore - Scanning of Real-World Sheet Music for a Digital Music Stand
Appears in Collections:Publikationen School of Engineering

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