Automatic bird song and syllable segmentation with an open-source deep-learning object detection method – a case study in the Collared Flycatcher (Ficedula albicollis)
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16 dic 2019
INFORMAZIONI SU QUESTO ARTICOLO
Pubblicato online: 16 dic 2019
Pagine: 59 - 66
Ricevuto: 12 set 2019
Accettato: 21 ott 2019
DOI: https://doi.org/10.2478/orhu-2019-0015
Parole chiave
© 2019 Sándor Zsebők et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Zsebők, Sándor
Behavioural Ecology Group, Department of Systematic Zoology and Ecology, Eötvös Loránd UniversityBudapest, Hungary
Nagy-Egri, Máté Ferenc
Wigner Research Centre for PhysicsBudapest, Hungary
Barnaföldi, Gergely Gábor
Wigner Research Centre for PhysicsBudapest, Hungary
Laczi, Miklós
Behavioural Ecology Group, Department of Systematic Zoology and Ecology, Eötvös Loránd UniversityBudapest, Hungary
Orosztony, Hungary
Nagy, Gergely
Behavioural Ecology Group, Department of Systematic Zoology and Ecology, Eötvös Loránd UniversityBudapest, Hungary
Vaskuti, Éva
Behavioural Ecology Group, Department of Systematic Zoology and Ecology, Eötvös Loránd UniversityBudapest, Hungary
Garamszegi, László Zsolt
Behavioural Ecology Group, Department of Systematic Zoology and Ecology, Eötvös Loránd UniversityBudapest, Hungary
MTA-ELTE, Theoretical Biology and Evolutionary Ecology Research Group, Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös Loránd UniversityBudapest, Hungary
Evolutionary Ecology Group, Centre for Ecological Research, Institute of Ecology and BotanyHungary