Commenced in January 2007
Paper Count: 31447
Sperm Whale Signal Analysis: Comparison using the Auto Regressive model and the Daubechies 15 Wavelets Transform
Abstract:This article presents the results using a parametric approach and a Wavelet Transform in analysing signals emitting from the sperm whale. The extraction of intrinsic characteristics of these unique signals emitted by marine mammals is still at present a difficult exercise for various reasons: firstly, it concerns non-stationary signals, and secondly, these signals are obstructed by interfering background noise. In this article, we compare the advantages and disadvantages of both methods: Auto Regressive models and Wavelet Transform. These approaches serve as an alternative to the commonly used estimators which are based on the Fourier Transform for which the hypotheses necessary for its application are in certain cases, not sufficiently proven. These modern approaches provide effective results particularly for the periodic tracking of the signal's characteristics and notably when the signal-to-noise ratio negatively effects signal tracking. Our objectives are twofold. Our first goal is to identify the animal through its acoustic signature. This includes recognition of the marine mammal species and ultimately of the individual animal (within the species). The second is much more ambitious and directly involves the intervention of cetologists to study the sounds emitted by marine mammals in an effort to characterize their behaviour. We are working on an approach based on the recordings of marine mammal signals and the findings from this data result from the Wavelet Transform. This article will explore the reasons for using this approach. In addition, thanks to the use of new processors, these algorithms once heavy in calculation time can be integrated in a real-time system.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1055375Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1750
 R. J. Urick, Principles of Underwater Sound, 3rd ed., Ed. New York: McGraw-Hill, 1983.
 R. F. Coates, Underwater Acoustic Systems. Ed. London: McMillan, 1990.
 W. W. L. Au, R. W. Floyd, R. H. Penner, A. E. Murchison, "Measurements of echolocation signals of the Atlantic bottlenose dolphin," J. Acoust. Soc. Am., vol. 56, pp. 1289-90, 1974.
 D. K. Mellinger, Ch W. Clark, "A method for filtering bioacoustic transients by spectrogram image convolution," Oceans, 1993.
 L. A. Miller, J. Pristed, B. Mohl and A. Surlykke, "The click-sounds of narwhales in Inglefield Bay," Marine Mammal Sci., vol. 11, pp. 491- 502, 1995.
 I. Tokuda, T. Riede, J. Neubauer and MJ. Owren, "Nonlinear analysis of irregular animal vocalizations," J. Acoust. Soc. Am., vol. 111, pp. 2908- 19, 2002.
 G. S. Campbell, R. C. Gisiner, D. A. Helweg and L. L. Milette, "Acoustic identification of female steller sea lions," J. Acoust. Soc. Am., vol 111, pp. 2920-28, 2002.
 R. Backus, W. E. Schevill, "Physeter clics," Whales, Porpoises and Dolphins, Ed. Univ. Calif. by K. S. Norris, pp. 510-28, 1966.
 W. Watkins, W. Schevill, "Sperm whale codas," J. Acoust. Soc. Am., vol. 62, 1977.
 B. Mohl, M. Wahlberg, P. T. Madsen, "Sperm Whale Clicks: directionality and source level revisited," J. Acoust. Soc. Am., pp. 638- 48, 2000.
R. Boileau, "Whale soundings," Zoological Physics, 438, 2002
 J. C. Goold, S. Jones, "Time and frequency domain characteristic of sperm whale clicks," J. Acoust. Soc. Am., pp. 1279-91, 1996.
 J. C. Goold, J. D. Bennell, S. E. Jones, "Sound velocity measurements in spermaceti oil under the combined influences of temperature and pressure," Deep-Sea Res., vol. 43, pp. 1279-1291, 1996.
 K. S. Norris, G. W. Harvey, "A theory for the function of the spermaceti organ of the sperm whale," Physeter Catodon, Ed. Washington DC: Nasa special publications, vol 262, pp. 397-417, 1972.
 W. Lauterborn, U. Parlitz, "Methods of chaos physics and their applications to acoustic," J. Acoust. Soc. Am., vol 84, pp. 1975-93, 1988.
 H. Kantz, T. Schreiber, Nonlinear Times Series Analysis, Ed. Cambridge PU, 1997.
 C. Tiemann, M. Porter, L. Frazer, "Automated Model-Based Localization of Marine Mammals near Hawaï," in Proc. of Oceans 2001 Conference, Hawaï, 2001
 J. Ward, K. Fitspatrick, N. DiMarzio, "New algorithms for open ocean marine mammal monitoring," in Proc. of Oceans Conference 2000.
 M. Lopatka, "Reconnaissance de signatures acoustiques pour la distinction d'individus dans un groupe de cachalots,", iSnS report, University Paris 12, France, 2002.
 S. Haykin, Signal Processing, Ed. IEEE Press, 1994.
 J. Max, Méthodes et techniques de traitement du signal et applications aux mesures physiques, Paris: Ed. Masson, 1982.
 ID. Landau, Identification et commandes des systèmes, Paris: Ed. Hermes, 1988.
 H. Akaïke, "A new look at the statistical model identification," IEEE Trans. on Auto. Control, vol.6, pp. 716-23, 1974.
 Y. Meyer, Les ondelettes, Algorithmes et Applications, Paris: Ed. Armand Colin, 1992.
 I. Daubechies, Ten Lectures on Wavelets, Philadelphia: Ed. Society for Industrial and Applied Mathematics, 1992
 J. Herault, C. Jutten, Réseaux neuronaux et traitement du signal, Paris: Ed. Hermes, 1994
 D.E. Rumelhart, G.E. Hinton, R.J. Williams, "Learning representations by back-propagation errors," Nature, vol. 323, pp. 533-36, 1986.
 O. Adam, "Approche compare des techniques connexionnistes et adaptatives pour le traitement des signaux lidar," Thesis, University Paris VI, 1995.