Automatic bird song and syllable segmentation with an open-source deep-learning object detection method – a case study in the Collared Flycatcher (Ficedula albicollis)
Authors
Sándor Zsebők
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
Language: English
Page range: 59 - 66
Submitted on: Sep 12, 2019
Accepted on: Oct 21, 2019
Published on: Dec 16, 2019
Published by: MME/BirdLife Hungary
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Related subjects:
© 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.