Ornithology meets the IoT: Automatic Bird Identification, Census, and Localization

Autor(en): Bruggemann, L.
Schutz, B.
Aschenbruck, N. 
Stichwörter: Audio acoustics; Biodiversity; Ecosystems; Internet of things; Surveys; Wireless sensor networks, Acoustic Sensors; Audio samples; Bird species; Cost-efficient; Ecosystem health; Environmental challenges; Localisation; Long term monitoring; Sensor systems; Wireless acoustic sensor networks, Birds
Erscheinungsdatum: 2021
Herausgeber: Institute of Electrical and Electronics Engineers Inc.
Journal: 7th IEEE World Forum on Internet of Things, WF-IoT 2021
Startseite: 765
Seitenende: 770
Stopping the unprecedented decline of biodiversity is one of the biggest environmental challenges in the 21st century. Regional monitoring of selected bird species is one key to analyze the causality and detect changes because their presence is a good indicator of ecosystem health and integrity. To enable cost-efficient, long-term monitoring, this paper presents a Wireless Acoustic Sensor System (WASS) for automated remote bird identification, census, and localization. The proposed system is based on a Wireless Acoustic Sensor Network (WASN) to record and transmit the audio samples combined with a classification framework for automated evaluation. This paper does not only discuss the technical requirements and introduces a user-friendly system architecture but also presents a first prototype. Its functionality is assessed in a short outdoor deployment. The measurements prove that the proposed system is capable of monitoring and classifying the present species automatically. Still, there are some limitations, and future refinements are pointed out. © 2021 IEEE.
Conference of 7th IEEE World Forum on Internet of Things, WF-IoT 2021 ; Conference Date: 14 June 2021 Through 31 July 2021; Conference Code:173945
ISBN: 9781665444316
DOI: 10.1109/WF-IoT51360.2021.9595401
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119861319&doi=10.1109%2fWF-IoT51360.2021.9595401&partnerID=40&md5=707b216fb811043dadb85d12f9a9bd5d

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