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https://doi.org/10.21256/zhaw-3760| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Deep watershed detector for music object recognition |
| Autor/-in: | Tuggener, Lukas Elezi, Ismail Schmidhuber, Jürgen Stadelmann, Thilo |
| DOI: | 10.21256/zhaw-3760 |
| Tagungsband: | Proceedings of the 19th International Society for Music Information Retrieval Conference |
| Angaben zur Konferenz: | 19th International Society for Music Information Retrieval Conference, Paris, 23-27 September 2018 |
| Erscheinungsdatum: | 2018 |
| Verlag / Hrsg. Institution: | Society for Music Information Retrieval |
| Verlag / Hrsg. Institution: | Paris |
| Sprache: | Englisch |
| Schlagwörter: | Optical music recognition; Deep learning |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/7085 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) |
| Publiziert im Rahmen des ZHAW-Projekts: | DeepScore: Digitales Notenpult mit musikalischem Verständnis durch Active Sheet Technologie |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| ISMIR_2018.pdf | 881.18 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Tuggener, L., Elezi, I., Schmidhuber, J., & Stadelmann, T. (2018). Deep watershed detector for music object recognition. Proceedings of the 19th International Society for Music Information Retrieval Conference. https://doi.org/10.21256/zhaw-3760
Tuggener, L. et al. (2018) ‘Deep watershed detector for music object recognition’, in Proceedings of the 19th International Society for Music Information Retrieval Conference. Paris: Society for Music Information Retrieval. Available at: https://doi.org/10.21256/zhaw-3760.
L. Tuggener, I. Elezi, J. Schmidhuber, and T. Stadelmann, “Deep watershed detector for music object recognition,” in Proceedings of the 19th International Society for Music Information Retrieval Conference, 2018. doi: 10.21256/zhaw-3760.
TUGGENER, Lukas, Ismail ELEZI, Jürgen SCHMIDHUBER und Thilo STADELMANN, 2018. Deep watershed detector for music object recognition. In: Proceedings of the 19th International Society for Music Information Retrieval Conference. Conference paper. Paris: Society for Music Information Retrieval. 2018
Tuggener, Lukas, Ismail Elezi, Jürgen Schmidhuber, and Thilo Stadelmann. 2018. “Deep Watershed Detector for Music Object Recognition.” Conference paper. In Proceedings of the 19th International Society for Music Information Retrieval Conference. Paris: Society for Music Information Retrieval. https://doi.org/10.21256/zhaw-3760.
Tuggener, Lukas, et al. “Deep Watershed Detector for Music Object Recognition.” Proceedings of the 19th International Society for Music Information Retrieval Conference, Society for Music Information Retrieval, 2018, https://doi.org/10.21256/zhaw-3760.
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