Title:	Automated Bioacoustic Monitoring for South African Bird Species on Unlabeled Data
Authors:	Doell, Michael Kuehn, Dominik Suessle, Vanessa Burnett, Matthew J. Downs, Colleen T. Weinmann, Andreas Hergenroether, Elke
Citation:	WSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 23-32.
Issue Date:	2024
Publisher:	Václav Skala - UNION Agency
URI:	http://hdl.handle.net/11025/57381
ISSN:	2464–4625 (online) 2464–4617 (print)
Keywords:	bioakustické monitorování;druhová klasifikace;spektrogramy;konvoluční rekurentní neuronová síť;obousměrná GRU;ekologie;zachování divoké zvěře
Keywords in different language:	bioacoustic monitoring;species classification;spectrograms;CNNs;convolutional recurrent neural network;bidirectional GRU;ecology;wildlife conservation
Abstract in different language:	Analyses for biodiversity monitoring based on passive acoustic monitoring (PAM) recordings is time-consuming and chal lenged by the presence of background noise in recordings. Existing models for sound event detection (SED) worked only on certain avian species and the development of further models required labeled data. The developed framework automatically extracted labeled data from available platforms for selected avian species. The labeled data were embedded into recordings, including environmental sounds and noise, and were used to train convolutional recurrent neural network (CRNN) models. The models were evaluated on unprocessed real world data recorded in urban KwaZulu-Natal habitats. The Adapted SED-CRNN model reached a F1 score of 0.73, demonstrating its efficiency under noisy, real-world conditions. The proposed approach to automatically extract labeled data for chosen avian species enables an easy adaption of PAM to other species and habitats for future conservation projects.
Appears in Collections:	WSCG 2024: Full Papers Proceedings

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