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A vocal-based analytical method for goose behaviour recognition.

Steen KA, Therkildsen OR, Karstoft H, Green O - Sensors (Basel) (2012)

Bottom Line: 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.The Support Vector Machine has proven to be a robust classifier for this kind of classification, as generality and non-linear capabilities 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 a wildlife management system.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering, Aarhus University, Aarhus N, Denmark. kima.steen@agrsci.dk

ABSTRACT
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-linear capabilities 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 a wildlife management system.

No MeSH data available.


Related in: MedlinePlus

Plot of the first three principal components of the extracted features after feature selection has taken place. It can be seen from the plot, that it is possible to discriminate between the three behaviours, however the vocalisations for landing and fleeing have some similarities.
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f6-sensors-12-03773: Plot of the first three principal components of the extracted features after feature selection has taken place. It can be seen from the plot, that it is possible to discriminate between the three behaviours, however the vocalisations for landing and fleeing have some similarities.

Mentions: The GFCC feature extraction makes it possible to discriminate between the vocalisations of the described behaviours. This is visualised in Figure 6, where the three first principal components of the selected features, are shown. The principal components are derived via principal component analysis (PCA) [47], and are the linear combination of the selected features which preserves the most variance in a smaller dimensional space. In Figure 6, it is seen that foraging behaviour seems easiest to discriminate.


A vocal-based analytical method for goose behaviour recognition.

Steen KA, Therkildsen OR, Karstoft H, Green O - Sensors (Basel) (2012)

Plot of the first three principal components of the extracted features after feature selection has taken place. It can be seen from the plot, that it is possible to discriminate between the three behaviours, however the vocalisations for landing and fleeing have some similarities.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376600&req=5

f6-sensors-12-03773: Plot of the first three principal components of the extracted features after feature selection has taken place. It can be seen from the plot, that it is possible to discriminate between the three behaviours, however the vocalisations for landing and fleeing have some similarities.
Mentions: The GFCC feature extraction makes it possible to discriminate between the vocalisations of the described behaviours. This is visualised in Figure 6, where the three first principal components of the selected features, are shown. The principal components are derived via principal component analysis (PCA) [47], and are the linear combination of the selected features which preserves the most variance in a smaller dimensional space. In Figure 6, it is seen that foraging behaviour seems easiest to discriminate.

Bottom Line: 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.The Support Vector Machine has proven to be a robust classifier for this kind of classification, as generality and non-linear capabilities 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 a wildlife management system.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering, Aarhus University, Aarhus N, Denmark. kima.steen@agrsci.dk

ABSTRACT
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-linear capabilities 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 a wildlife management system.

No MeSH data available.


Related in: MedlinePlus