Classifying and ranking audio clips to support bird species richness surveys

Zhang, Liang, Towsey, Michael, Zhang, Jinglan, & Roe, Paul (2016) Classifying and ranking audio clips to support bird species richness surveys. Ecological Informatics, 34, pp. 108-116.

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Advances in programmable field acoustic sensors provide immense data for bird species study. Manually searching for bird species present in these acoustic data is time-consuming. Although automated techniques have been used for species recognition in many studies, currently these techniques are prone to error due to the complexity of natural acoustics.

In this paper we propose a smart sampling approach to help identify the maximum number of bird species while listening to the minimum amount of acoustic data. This approach samples audio clips in a manner that can direct bird species surveys more efficiently. First, a classifier is built to remove audio clips that are unlikely to contain birds; second, the remaining audio clips are ranked by a proxy for the number of species. This technique enables a more efficient determination of species richness. The experimental results show that the use of a classifier enables to remove redundant acoustic data and make our approach resilient to various weather conditions. By ranking audio clips classified as “Birds”, our method outperforms the currently best published strategy for finding bird species after 30 one-minute audio clip samples. Particularly after 60 samples, our method achieves 10 percentage points more species. Despite our focus on bird species, the proposed sampling approach is applicable to the search of other vocal species.

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ID Code: 96243
Item Type: Journal Article
Refereed: Yes
Keywords: Acoustic sampling, Classification, Acoustic monitoring, Ranking, Bird species richness
DOI: 10.1016/j.ecoinf.2016.05.005
ISSN: 1574-9541
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2016 Elsevier
Copyright Statement: Licensed under the Creative Commons Attribution; Non-Commercial; No-Derivatives 4.0 International. DOI: 10.1016/j.ecoinf.2016.05.005
Deposited On: 19 Jun 2016 23:27
Last Modified: 23 Jun 2016 06:03

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