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Automatic bird song and syllable segmentation with an open-source deep-learning object detection method – a case study in the Collared Flycatcher (Ficedula albicollis) Cover

Automatic bird song and syllable segmentation with an open-source deep-learning object detection method – a case study in the Collared Flycatcher (Ficedula albicollis)

Open Access
|Dec 2019

Authors

Sándor Zsebők

zsebok.s@gmail.com

Behavioural Ecology Group, Department of Systematic Zoology and Ecology, Eötvös Loránd University, Budapest, Hungary

Máté Ferenc Nagy-Egri

Wigner Research Centre for Physics, Budapest, Hungary

Gergely Gábor Barnaföldi

Wigner Research Centre for Physics, Budapest, Hungary

Miklós Laczi

Behavioural Ecology Group, Department of Systematic Zoology and Ecology, Eötvös Loránd University, Budapest, Hungary
Orosztony, Hungary

Gergely Nagy

Behavioural Ecology Group, Department of Systematic Zoology and Ecology, Eötvös Loránd University, Budapest, Hungary

Éva Vaskuti

Behavioural Ecology Group, Department of Systematic Zoology and Ecology, Eötvös Loránd University, Budapest, Hungary

László Zsolt Garamszegi

Behavioural Ecology Group, Department of Systematic Zoology and Ecology, Eötvös Loránd University, Budapest, Hungary
MTA-ELTE, Theoretical Biology and Evolutionary Ecology Research Group, Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös Loránd University, Budapest, Hungary
Evolutionary Ecology Group, Centre for Ecological Research, Institute of Ecology and Botany, Hungary
DOI: https://doi.org/10.2478/orhu-2019-0015 | Journal eISSN: 2061-9588 | Journal ISSN: 1215-1610
Language: English
Page range: 59 - 66
Submitted on: Sep 12, 2019
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Accepted on: Oct 21, 2019
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Published on: Dec 16, 2019
Published by: MME/BirdLife Hungary
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2019 Sándor Zsebők, Máté Ferenc Nagy-Egri, Gergely Gábor Barnaföldi, Miklós Laczi, Gergely Nagy, Éva Vaskuti, László Zsolt Garamszegi, published by MME/BirdLife Hungary
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.