Next Article in Journal
Resonant Frequency Characteristics of a SAW Device Attached to Resonating Micropillars
Previous Article in Journal
A Pro-Drug Approach for Selective Modulation of AI-2-Mediated Bacterial Cell-to-Cell Communication
Article Menu

Export Article

Open AccessArticle
Sensors 2012, 12(3), 3773-3788;

A Vocal-Based Analytical Method for Goose Behaviour Recognition

Department of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark
Department of Bioscience, Aarhus University, Grenåvej 14, 8410 Rønde, Denmark
Author to whom correspondence should be addressed.
Received: 9 February 2012 / Revised: 7 March 2012 / Accepted: 20 March 2012 / Published: 21 March 2012
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [625 KB, uploaded 21 June 2014]


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
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Supplementary materials


Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top