Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3760
Title: Deep watershed detector for music object recognition
Authors : Tuggener, Lukas
Elezi, Ismail
Schmidhuber, Jürgen
Stadelmann, Thilo
Proceedings: Proceedings of the 19th International Society for Music Information Retrieval Conference
Conference details: 19th International Society for Music Information Retrieval Conference, Paris, 23. - 27. September 2018
Publisher / Ed. Institution : Society for Music Information Retrieval
Publisher / Ed. Institution: Paris
Issue Date: 2018
License (according to publishing contract) : CC BY 4.0: Namensnennung 4.0 International
Type of review: Peer review (Publication)
Language : English
Subjects : Optical music recognition; Deep learning
Subject (DDC) : 004: Computer science
Abstract: Optical Music Recognition (OMR) is an important and challenging area within music information retrieval, the accurate detection of music symbols in digital images is a core functionality of any OMR pipeline. In this paper, we introduce a novel object detection method, based on synthetic energy maps and the watershed transform, called Deep Watershed Detector (DWD). Our method is specifically tailored to deal with high resolution images that contain a large number of very small objects and is therefore able to process full pages of written music. We present state-of-the-art detection results of common music symbols and show DWD’s ability to work with synthetic scores equally well as on handwritten music.
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Publication type: Conference Paper
DOI : 10.21256/zhaw-3760
URI: https://digitalcollection.zhaw.ch/handle/11475/7085
Published as part of the ZHAW project : DeepScore: Digitales Notenpult mit musikalischem Verständnis durch Active Sheet Technologie
Appears in Collections:Publikationen School of Engineering

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