Computer-assisted sampling of acoustic data for more efficient determination of bird species richness

Zhang, Liang, Towsey, Michael, Zhang, Jinglan, & Roe, Paul (2015) Computer-assisted sampling of acoustic data for more efficient determination of bird species richness. In Proceedings of the 2015 IEEE 15th International Conference on Data Mining Workshops, IEEE, Atlantic City, N.J, pp. 798-805.

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Bird species richness survey is one of the most intriguing ecological topics for evaluating environmental health. Here, bird species richness denotes the number of unique bird species in a particular area. Factors affecting the investigation of bird species richness include weather, observation bias, and most importantly, the prohibitive costs of conducting surveys at large spatiotemporal scales. Thanks to advances in recording techniques, these problems have been alleviated by deploying sensors for acoustic data collection. Although automated detection techniques have been introduced to identify various bird species, the innate complexity of bird vocalizations, the background noise present in the recording and the escalating volumes of acoustic data pose a challenging task on determination of bird species richness. In this paper we proposed a two-step computer-assisted sampling approach for determining bird species richness in one-day acoustic data. First, a classification model is built based on acoustic indices for filtering out minutes that contain few bird species. Then the classified bird minutes are ordered by an acoustic index and the redundant temporal minutes are removed from the ranked minute sequence. The experimental results show that our method is more efficient in directing experts for determination of bird species compared with the previous methods.

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ID Code: 89569
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Acoustic Indices, Classification, Bird species richness, Acoustic sampling
DOI: 10.1109/ICDMW.2015.42
ISBN: 978-1-4673-8492-6
Subjects: Australian and New Zealand Standard Research Classification > ENVIRONMENTAL SCIENCES (050000) > ENVIRONMENTAL SCIENCE AND MANAGEMENT (050200) > Conservation and Biodiversity (050202)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 [Please consult the author]
Deposited On: 27 Oct 2015 23:45
Last Modified: 23 Mar 2016 16:30

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