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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

Block diagram of the acoustic feature extraction performed on the recorded vocalisations. A total of 21 features were extracted and six features were chosen based on feature selection techniques.
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f3-sensors-12-03773: Block diagram of the acoustic feature extraction performed on the recorded vocalisations. A total of 21 features were extracted and six features were chosen based on feature selection techniques.

Mentions: The calculation of GFCC is illustrated in Figure 3, where the incoming signal has a duration of 46 ms (2048 samples), as cepstral coefficients are derived from short-time analysis. The log-energy of each critical band is represented by spectral vectors, and a cosine transform converts the spectral vectors into cepstral vectors, according to the formula(11)cn=∑k=0K−1Sk cos (n(k−12)πK)    n=0,…,K−1Here cn is the nth cepstral coefficients and Sk is the spectral log-energy of the kth band. In this research 20 critical band filters were used, which gives a feature vector of dimension 21, as the 0th order cepstral coefficient is included (see Brookes [42]). The filters were hamming shaped, however both hanning and triangle shaped filters are often used in MFCC feature extraction [35].


A vocal-based analytical method for goose behaviour recognition.

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

Block diagram of the acoustic feature extraction performed on the recorded vocalisations. A total of 21 features were extracted and six features were chosen based on feature selection techniques.
© Copyright Policy
Related In: Results  -  Collection

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

f3-sensors-12-03773: Block diagram of the acoustic feature extraction performed on the recorded vocalisations. A total of 21 features were extracted and six features were chosen based on feature selection techniques.
Mentions: The calculation of GFCC is illustrated in Figure 3, where the incoming signal has a duration of 46 ms (2048 samples), as cepstral coefficients are derived from short-time analysis. The log-energy of each critical band is represented by spectral vectors, and a cosine transform converts the spectral vectors into cepstral vectors, according to the formula(11)cn=∑k=0K−1Sk cos (n(k−12)πK)    n=0,…,K−1Here cn is the nth cepstral coefficients and Sk is the spectral log-energy of the kth band. In this research 20 critical band filters were used, which gives a feature vector of dimension 21, as the 0th order cepstral coefficient is included (see Brookes [42]). The filters were hamming shaped, however both hanning and triangle shaped filters are often used in MFCC feature extraction [35].

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