A vocal-based analytical method for goose behaviour recognition

Sensors

Volumn 12, Issue 3, 2012, Pages 3773-3788

  Steen, Kim Arild ^a   Therkildsen, Ole Roland ^a   Karstoft, Henrik ^a   Green, Ole ^a  

^a AARHUS UNIVERSITY   (Denmark)

Author keywords

GFCC; Goose behaviour; Pattern recognition; Support vector machines; Vocalisations

Indexed keywords

EID: 84859087288     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s120303773     Document Type: Article

References (49)

1. Messmer, T.A. The emergence of human-wildlife conflict management: Turning challenges into opportunities. Int. Biodeteriro. Biodegrad. 2000, 45, 97-102.
2. Gilsdorf, J.M.; Hygnstrom, S.E.; Vercauteren, K.C. Use of frightening devices in wildlife damage management. Integr. Pest Manag. Rev. 2002, 7, 29-45.
3. Nolte, D. Behavioral Approaches For Limiting Depredation by Wild Ungulates. In Grazing Behavior of Livestock and Wildlife; Launchbaugh, K., Sanders, K., Mosley, J., Eds.; University of Idaho: Moscow, ID, USA, 1999; pp. 60-69.
4. Rutter, S.M. Revista Brasileira de Zootecnia The integration of GPS, vegetation mapping and GIS. Rev. Bras. Zootec. 2007, 36, 63-70.
5. Nadimi, E.; Søgaard, H.; Bak, T. ZigBee-based wireless sensor networks for classifying the behaviour of a herd of animals using classication trees. Biosyst. Eng. 2008, 100, 167-176.
6. Guo, Y.; Corke, P.; Poulton, G.; Wark, T.; Swain, D.; Corke, P.; Poulton, G.; Wark, T. Animal Behaviour Understanding Using Wireless Sensor Networks. In Proceedings of the 2006 31st IEEE Conference on Local Computer Networks, Tampa, FL, USA, 14-16 November 2006; pp. 607-614.
7. Müller, R.; Schrader, L. A new method to measure behavioural activity levels in dairy cows. Appl. Anim. Behav. Sci. 2003, 83, 247-258.
8. David, E.; Mark, S. Classifying cattle jaw movements: Comparing IGER behaviour recorder and acoustic techniques. Appl. Anim. Behav. Sci. 2006, 98, 11-27.
9. Vallejo, E.E.; Taylor, C.E. Adaptive sensor arrays for acoustic monitoring of bird behavior and diversity: Preliminary results on source identification using support vector machines. Artif. Life Robot. 2010, 14, 485-488.
10. Ren, Y.; Johnson, M.T.; Clemins, P.; Darre, M.; Glaeser, S.S.; Osiejuk, T.S.; Out-Nyarko, E. A framework for bioacoustic vocalization analysis using hidden markov models. Algorithms 2009, 2, 1410-1428.
11. Milone, D.; Rufiner, H.; Galli, J.; Laca, E.; Cangiano, C. Computational method for segmentation and classification of ingestive sounds in sheep. Comput. Electron. Agric. 2009, 65, 228-237.
12. Schon, P.C.; Puppe, B.; Manteuffel, G. Linear prediction coding analysis and self-organizing feature map as tools to classify stress calls of domestic pigs (Sus scrofa). J. Acoust. Soc. Am. 2001, 110, 1425-1431.
13. Kogan, J.A.; Margoliash, D. Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: A comparative study. J. Acoust. Soc. Am. 1998, 103, 2185-2196.
14. Thomas, R.E.; Fristrup, K.M.; Tyack, P.L. Linking the sounds of dolphins to their locations and behavior using video and multichannel acoustic recordings. J. Acoust. Soc. Am. 2002, 112, 1692-1701.
15. Manteuffel, G. Vocalization of farm animals as a measure of welfare. Appl. Anim. Behav. Sci. 2004, 88, 163-182.
16. Manteuffel, G.; Schön, P.C. Measuring pig welfare by automatic monitoring of stress calls. Bornimer Agrartech. Ber. 2002, 29, 110-118.
17. Moura, D.; Silva, W.; Naas, I.; Tolon, Y.; Lima, K.; Vale, M. Real time computer stress monitoring of piglets using vocalization analysis. Comput. Electron. Agric. 2008, 64, 11-18.
18. Reby, D.; André-Obrecht, R.; Galinier, A.; Farinas, J.; Cargnelutti, B. Cepstral coefficients and hidden Markov models reveal idiosyncratic voice characteristics in red deer (Cervus elaphus) stags. J. Acoust. Soc. Am. 2006, 120, 4080-4089.
19. Lee, C.; Chou, C.; Han, C.; Huang, R. Automatic recognition of animal vocalizations using averaged MFCC and linear discriminant analysis. Pattern Recogn. Lett. 2006, 27, 93-101.
20. Brown, J.C.; Smaragdis, P. Hidden Markov and Gaussian mixture models for automatic call classification. J. Acoust. Soc. Am. 2009, 125, EL221-EL224.
21. Trifa, V.M.; Kirschel, A.N.G.; Taylor, C.E.; Vallejo, E.E. Automated species recognition of antbirds in a Mexican rainforest using hidden Markov models. J. Acoust. Soc. Am. 2008, 123, 2424-2431.
22. Fagerlund, S. Bird species recognition using support vector machines. EURASIP J. Adv. Signal Process. 2007, 2007, 1-9.
23. Campbell, W.M. Speaker verification using support vector machines and high-level features. IEEE Trans. Audio Speech Lang. Process. 2007, 10, 1641-2094.
24. Clemins, P.; Trawicki, M.; Adi, K.; Johnson, M. Generalized Perceptual Features for Vocalization Analysis Across Multiple Species. In Proceedings of the 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, Toulouse, France, 14-19 May 2006; pp. I-253-I-256.
25. Burges, C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining Knowl. Discov. 1998, 2, 121-167.
26. Theodoridis, S.; Koutroumbas, K. Pattern Recognition, 4th ed.; Academic Press: Waltham, MA, USA, 2008.
27. Vapnik, V.N. The Nature of Statistical Learning Theory; Springer-Verlag Inc.: New York, NY, USA, 1995.
28. Martiskainen, P.; Järvinen, M.; Skön, J.P.; Tiirikainen, J.; Kolehmainen, M.; Mononen, J. Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. Appl. Anim. Behav. Sci. 2009, 119, 32-38.
29. Blanz, V.; Scholkopf, B.; Bultho, H.; Burges, C.; Vapnik, V.; Vetter, T. Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models. In Proceedings of the International Conference on Artifiial Neural Networks, Bochum, Germany, 16-19 July 1996; pp. 251-256.
30. Steen, K.A.; Karstoft, H.; Green, O. A Multimedia Capture System for Wildlife Studies. In Proceedings of the Third International Conference on Emerging Network Intelligence, Lisbon, Portugal, 20-25 November 2011; Number c, pp. 131-134.
31. Cortes, C.; Vapnik, V. Support vector networks. Mach. Learn. 1995, 20, 273-297.
32. Bimbot, F.; Bonastre, J.F.; Fredouille, C.; Gravier, G.; Magrin-Chagnolleau, I.; Meignier, S.; Merlin, T.; Ortega-García, J.; Petrovska-Delacrétaz, D.; Reynolds, D.A. A tutorial on text-independent speaker verification. EURASIP J. Adv. Signal Process. 2004, 2004, 430-451.
33. Deller, J.R., Jr.; Proakis, J.G.; Hansen, J.H. Discrete Time Processing of Speech Signals, 1st ed.; Prentice Hall PTR: Upper Saddle River, NJ, USA, 1993.
34. Fant, C. Analysis and Synthesis of Speech Processes. In Manual of Phonetics; Malmberg, B., (Ed.); North-Holland: Amsterdam, the Netherlands, 1968; pp. 173-277.
35. Ganchev, T.; Fakotakis, N.; Kokkinakis, G. Comparative Evaluation of Various MFCC Implementations on the Speaker Verification Task. In Proceedings of the Proceedings of Speech and Computer, Patras, Greece, 17-19 October 2005; Volume 1, pp. 191-194.
36. Dmitrieva, L.P.; Gottlieb, G. Influence of auditory experience on the development of brain stem auditory-evoked potentials in mallard duck embryos and hatchlings. Behav. Neural Biol. 1994, 61, 19-28.
37. Davis, S.; Mermelstein, P. Comparison of parametric representations for monosyllabic word recognition in continuosly spoken sentences. IEEE Trans. Acoust. Speech Signal Process. 1980, 28, 357-366.
38. Zeng, Z.; Pantic, M.; Roisman, G.I.; Huang, T.S. A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31, 39-58.
39. Greenwood, D.D. Critical bandwidth and consonance in relation to cochlear frequency-position coordinates. Hear. Res. 1991, 54, 164-208.
40. Adi, K.; Johnson, M.T.; Osiejuk, T.S. Acoustic censusing using automatic vocalization classification and identity recognition. J. Acoust. Soc. Am. 2010, 127, pp. 874-883.
41. LePage, E.L. The mammalian cochlear map is optimally warped. J. Acoust. Soc. Am. 2003, 114, pp. 896-906.
42. Brookes, M. VOICEBOX: Speech Processing Toolbox for MATLAB; Techinical Report; University of London: London, UK, 1997-2011. Available online: http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox (accessed on 20 March 2012).
43. Chang, C.C.; Lin, C.J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 2, 1-27.
44. Hsu, C.W.; Chang, C.C.; Lin, C.J. A Practical Guide to Support Vector Classification; Technical Report; Department of Computer Science and Information Engineering, National Taiwan University: Taipei, Taiwan, 2010. Available online: http://www.csie.ntu.edu.tw/cjlin/papers/guide/guide.pdf (accessed on 20 March 2012).
45. Ben-Hur, A.; Weston, J. A User's Guide to Support Vector Machines. Methods Mol. Biol. 2008, 609, 223-239.
46. Platt, J.C.; Way, M.; Shawe-Taylor, J. Large Margin DAGs for Multiclass Classification; MIT Press: Cambridge, MA, USA, 2000; pp. 547-553.
47. Bishop, C.M. Pattern Recognition and Machine Learning (Information Science and Statistics); Springer-Verlag Inc.: Secaucus, NJ, USA, 2006.
48. Perner, P. Motion Tracking of Animals for Behavior Analysis. In Proceedings of the 4th International Workshop on Visual Form (IWVF4 '01), Capri, Italy, 28-30 May 2001; Volume 2059/2001, pp. 779-786.
49. Matetić, M.; Ribarić, S.; Ipšić, I. Qualitative modelling and analysis of animal behaviour. Appl. Intell. 2004, 21, 25-44.