{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T04:58:08Z","timestamp":1761541088945,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2012,3,21]],"date-time":"2012-03-21T00:00:00Z","timestamp":1332288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u201397% sensitivity, 89\u201398% precision) and a reasonable recognition of flushing (79\u201386%, 66\u201380%) and landing behaviour(73\u201391%, 79\u201392%). 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.<\/jats:p>","DOI":"10.3390\/s120303773","type":"journal-article","created":{"date-parts":[[2012,3,22]],"date-time":"2012-03-22T19:26:07Z","timestamp":1332444367000},"page":"3773-3788","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Vocal-Based Analytical Method for Goose Behaviour Recognition"],"prefix":"10.3390","volume":"12","author":[{"given":"Kim Arild","family":"Steen","sequence":"first","affiliation":[{"name":"Department of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark"}]},{"given":"Ole Roland","family":"Therkildsen","sequence":"additional","affiliation":[{"name":"Department of Bioscience, Aarhus University, Gren\u00e5vej 14, 8410 R\u00f8nde, Denmark"}]},{"given":"Henrik","family":"Karstoft","sequence":"additional","affiliation":[{"name":"Department of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark"}]},{"given":"Ole","family":"Green","sequence":"additional","affiliation":[{"name":"Department of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark"}]}],"member":"1968","published-online":{"date-parts":[[2012,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/S0964-8305(00)00045-7","article-title":"The emergence of human-wildlife conflict management: Turning challenges into opportunities","volume":"45","author":"Messmer","year":"2000","journal-title":"Int. Biodeteriro. Biodegrad"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1023\/A:1025760032566","article-title":"Use of frightening devices in wildlife damage management","volume":"7","author":"Gilsdorf","year":"2002","journal-title":"Integr. Pest Manag. Rev"},{"key":"ref_3","unstructured":"Launchbaugh, K., Sanders, K., and Mosley, J. (1999). Grazing Behavior of Livestock and Wildlife, University of Idaho."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1590\/S1516-35982007001000007","article-title":"Revista Brasileira de Zootecnia The integration of GPS, vegetation mapping and GIS","volume":"36","author":"Rutter","year":"2007","journal-title":"Rev. Bras. Zootec"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.biosystemseng.2008.03.003","article-title":"ZigBee-based wireless sensor networks for classifying the behaviour of a herd of animals using classication trees","volume":"100","author":"Nadimi","year":"2008","journal-title":"Biosyst. Eng"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Guo, Y., Corke, P., Poulton, G., Wark, T., Swain, D., Corke, P., Poulton, G., and Wark, T. (2006, January 14\u201316). Animal Behaviour Understanding Using Wireless Sensor Networks. Tampa, FL, USA.","DOI":"10.1109\/LCN.2006.322023"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/S0168-1591(03)00141-2","article-title":"A new method to measure behavioural activity levels in dairy cows","volume":"83","author":"Schrader","year":"2003","journal-title":"Appl. Anim. Behav. Sci"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.applanim.2005.08.011","article-title":"Classifying cattle jaw movements: Comparing IGER behaviour recorder and acoustic techniques","volume":"98","author":"David","year":"2006","journal-title":"Appl. Anim. Behav. Sci"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/s10015-009-0705-y","article-title":"Adaptive sensor arrays for acoustic monitoring of bird behavior and diversity: Preliminary results on source identification using support vector machines","volume":"14","author":"Vallejo","year":"2010","journal-title":"Artif. Life Robot"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1410","DOI":"10.3390\/a2041410","article-title":"A framework for bioacoustic vocalization analysis using hidden markov models","volume":"2","author":"Ren","year":"2009","journal-title":"Algorithms"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.compag.2008.10.004","article-title":"Computational method for segmentation and classification of ingestive sounds in sheep","volume":"65","author":"Milone","year":"2009","journal-title":"Comput. Electron. Agric"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1121\/1.1388003","article-title":"Linear prediction coding analysis and self-organizing feature map as tools to classify stress calls of domestic pigs (Sus scrofa)","volume":"110","author":"Schon","year":"2001","journal-title":"J. Acoust. Soc. Am"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1121\/1.421364","article-title":"Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: A comparative study","volume":"103","author":"Kogan","year":"1998","journal-title":"J. Acoust. Soc. Am"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1121\/1.1494805","article-title":"Linking the sounds of dolphins to their locations and behavior using video and multichannel acoustic recordings","volume":"112","author":"Thomas","year":"2002","journal-title":"J. Acoust. Soc. Am"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.applanim.2004.02.012","article-title":"Vocalization of farm animals as a measure of welfare","volume":"88","author":"Manteuffel","year":"2004","journal-title":"Appl. Anim. Behav. Sci"},{"key":"ref_16","first-page":"110","article-title":"Measuring pig welfare by automatic monitoring of stress calls","volume":"29","author":"Manteuffel","year":"2002","journal-title":"Bornimer Agrartech. Ber"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.compag.2008.05.008","article-title":"Real time computer stress monitoring of piglets using vocalization analysis","volume":"64","author":"Moura","year":"2008","journal-title":"Comput. Electron. Agric"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4080","DOI":"10.1121\/1.2358006","article-title":"Cepstral coefficients and hidden Markov models reveal idiosyncratic voice characteristics in red deer (Cervus elaphus) stags","volume":"120","author":"Reby","year":"2006","journal-title":"J. Acoust. Soc. Am"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.patrec.2005.07.004","article-title":"Automatic recognition of animal vocalizations using averaged MFCC and linear discriminant analysis","volume":"27","author":"Lee","year":"2006","journal-title":"Pattern Recogn. Lett"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"EL221","DOI":"10.1121\/1.3124659","article-title":"Hidden Markov and Gaussian mixture models for automatic call classification","volume":"125","author":"Brown","year":"2009","journal-title":"J. Acoust. Soc. Am"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2424","DOI":"10.1121\/1.2839017","article-title":"Automated species recognition of antbirds in a Mexican rainforest using hidden Markov models","volume":"123","author":"Trifa","year":"2008","journal-title":"J. Acoust. Soc. Am"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2007\/38637","article-title":"Bird species recognition using support vector machines","volume":"2007","author":"Fagerlund","year":"2007","journal-title":"EURASIP J. Adv. Signal Process"},{"key":"ref_23","first-page":"1641","article-title":"Speaker verification using support vector machines and high-level features","volume":"10","author":"Campbell","year":"2007","journal-title":"IEEE Trans. Audio Speech Lang. Process"},{"key":"ref_24","unstructured":"Clemins, P., Trawicki, M., Adi, K., and Johnson, M. (2006, January 14\u201319). Generalized Perceptual Features for Vocalization Analysis Across Multiple Species. Toulouse, France."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Mining Knowl. Discov"},{"key":"ref_26","unstructured":"Theodoridis, S., and Koutroumbas, K. (2008). Pattern Recognition, Academic Press. [4th ed]."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer-Verlag Inc.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.applanim.2009.03.005","article-title":"Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines","volume":"119","author":"Martiskainen","year":"2009","journal-title":"Appl. Anim. Behav. Sci"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Blanz, V., Scholkopf, B., Bultho, H., Burges, C., Vapnik, V., and Vetter, T. (1996, January 16\u201319). Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models. Bochum, Germany.","DOI":"10.1007\/3-540-61510-5_45"},{"key":"ref_30","unstructured":"Steen, K.A., Karstoft, H., and Green, O. (2011, January 20\u201325). A Multimedia Capture System for Wildlife Studies. Lisbon, Portugal. Number c,."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1155\/S1110865704310024","article-title":"A tutorial on text-independent speaker verification","volume":"2004","author":"Bimbot","year":"2004","journal-title":"EURASIP J. Adv. Signal Process"},{"key":"ref_33","unstructured":"Deller, J.R., Proakis, J.G., and Hansen, J.H. (1993). Discrete Time Processing of Speech Signals, Prentice Hall PTR. [1st ed]."},{"key":"ref_34","unstructured":"Malmberg, B. (1968). Manual of Phonetics, North-Holland."},{"key":"ref_35","unstructured":"Ganchev, T., Fakotakis, N., and Kokkinakis, G. (2005, January 17\u201319). Comparative Evaluation of Various MFCC Implementations on the Speaker Verification Task. Patras, Greece."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/S0163-1047(05)80040-8","article-title":"Influence of auditory experience on the development of brain stem auditory-evoked potentials in mallard duck embryos and hatchlings","volume":"61","author":"Dmitrieva","year":"1994","journal-title":"Behav. Neural Biol"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TASSP.1980.1163420","article-title":"Comparison of parametric representations for monosyllabic word recognition in continuosly spoken sentences","volume":"28","author":"Davis","year":"1980","journal-title":"IEEE Trans. Acoust. Speech Signal Process"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/TPAMI.2008.52","article-title":"A survey of affect recognition methods: Audio, visual, and spontaneous expressions","volume":"31","author":"Zeng","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/0378-5955(91)90117-R","article-title":"Critical bandwidth and consonance in relation to cochlear frequency-position coordinates","volume":"54","author":"Greenwood","year":"1991","journal-title":"Hear. Res"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1121\/1.3273887","article-title":"Acoustic censusing using automatic vocalization classification and identity recognition","volume":"127","author":"Adi","year":"2010","journal-title":"J. Acoust. Soc. Am"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1121\/1.1587150","article-title":"The mammalian cochlear map is optimally warped","volume":"114","author":"LePage","year":"2003","journal-title":"J. Acoust. Soc. Am"},{"key":"ref_42","unstructured":"Brookes, M. (1997\u20132011). VOICEBOX: Speech Processing Toolbox for MATLAB, University of London. Available online: http:\/\/www.ee.ic.ac.uk\/hp\/staff\/dmb\/voicebox\/voicebox (accessed on 20 March 2012)."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol"},{"key":"ref_44","unstructured":"Hsu, C.W., Chang, C.C., and Lin, C.J. (2010). A Practical Guide to Support Vector Classification, Department of Computer Science and Information Engineering, National Taiwan University. Available online: http:\/\/www.csie.ntu.edu.tw\/cjlin\/papers\/guide\/guide.pdf (accessed on 20 March 2012)."},{"key":"ref_45","first-page":"223","article-title":"A User\u2019s Guide to Support Vector Machines","volume":"609","author":"Weston","year":"2008","journal-title":"Methods Mol. Biol"},{"key":"ref_46","unstructured":"Platt, J.C., Way, M., and Shawe-Taylor, J. (2000). Large Margin DAGs for Multiclass Classification, MIT Press."},{"key":"ref_47","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag Inc."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Perner, P. (2001, January 28\u201330). Motion Tracking of Animals for Behavior Analysis. Capri, Italy.","DOI":"10.1007\/3-540-45129-3_72"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1023\/B:APIN.0000027765.12621.6f","article-title":"Qualitative modelling and analysis of animal behaviour","volume":"21","year":"2004","journal-title":"Appl. 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