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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

Concept of classification of landing behaviour, based on recorded vocalisations.
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f1-sensors-12-03773: Concept of classification of landing behaviour, based on recorded vocalisations.

Mentions: For our purpose, we identified three relevant behaviours (landing, foraging and flushing), which are all accompanied by distinct vocalisations easily identified by the human ear. The vocalisations allow us to identify a flock of geese (1) attempting to land; (2) foraging or (3) being flushed. By using vocalisation recognition, we are then able to automatically detect a flock of geese attempting to land and to assess the effect of a scaring (see Figure 1). Thereby, the concept allows us to monitor potential habituation (i.e., the situation, when geese no longer respond to scaring) and, accordingly, change our scaring strategy.


A vocal-based analytical method for goose behaviour recognition.

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

Concept of classification of landing behaviour, based on recorded vocalisations.
© Copyright Policy
Related In: Results  -  Collection

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

f1-sensors-12-03773: Concept of classification of landing behaviour, based on recorded vocalisations.
Mentions: For our purpose, we identified three relevant behaviours (landing, foraging and flushing), which are all accompanied by distinct vocalisations easily identified by the human ear. The vocalisations allow us to identify a flock of geese (1) attempting to land; (2) foraging or (3) being flushed. By using vocalisation recognition, we are then able to automatically detect a flock of geese attempting to land and to assess the effect of a scaring (see Figure 1). Thereby, the concept allows us to monitor potential habituation (i.e., the situation, when geese no longer respond to scaring) and, accordingly, change our scaring strategy.

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