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Open AccessArticle

A Vocal-Based Analytical Method for Goose Behaviour Recognition

1
Department of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark
2
Department of Bioscience, Aarhus University, Grenåvej 14, 8410 Rønde, Denmark
*
Author to whom correspondence should be addressed.
Sensors 2012, 12(3), 3773-3788; https://doi.org/10.3390/s120303773
Received: 9 February 2012 / Revised: 7 March 2012 / Accepted: 20 March 2012 / Published: 21 March 2012
(This article belongs to the Section Physical Sensors)
Since human-wildlife conflicts are increasing, the development of cost-effective methods for reducing damage or conflict levels is important in wildlife management. A wide range of devices to detect and deter animals causing conflict are used for this purpose, although their effectiveness is often highly variable, due to habituation to disruptive or disturbing stimuli. Automated recognition of behaviours could form a critical component of a system capable of altering the disruptive stimuli to avoid this. In this paper we present a novel method to automatically recognise goose behaviour based on vocalisations from flocks of free-living barnacle geese (Branta leucopsis). The geese were observed and recorded in a natural environment, using a shielded shotgun microphone. The classification used Support Vector Machines (SVMs), which had been trained with labeled data. Greenwood Function Cepstral Coefficients (GFCC) were used as features for the pattern recognition algorithm, as they can be adjusted to the hearing capabilities of different species. Three behaviours are classified based in this approach, and the method achieves a good recognition of foraging behaviour (86–97% sensitivity, 89–98% precision) and a reasonable recognition of flushing (79–86%, 66–80%) and landing behaviour(73–91%, 79–92%). The Support Vector Machine has proven to be a robust classifier for this kind of classification, as generality and non-linearcapabilities are important. We conclude that vocalisations can be used to automatically detect behaviour of conflict wildlife species, and as such, may be used as an integrated part of awildlife management system. View Full-Text
Keywords: support vector machines; goose behaviour; pattern recognition; vocalisations; GFCC support vector machines; goose behaviour; pattern recognition; vocalisations; GFCC
MDPI and ACS Style

Steen, K.A.; Therkildsen, O.R.; Karstoft, H.; Green, O. A Vocal-Based Analytical Method for Goose Behaviour Recognition. Sensors 2012, 12, 3773-3788.

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