Sperm Whale Signal Analysis: Comparison using the Auto Regressive model and the Daubechies 15 Wavelets Transform

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.

Authors:

• Olivier Adam
• Maciej Lopatka
• Christophe Laplanche
• Jean-François Motsch

Keywords:

• Autoregressive model
• Daubechies Wavelet
• Fourier Transform
• marine mammals
• signal processing
• spectrogram
• sperm whale
• Wavelet Transform.

References:

[1] R. J. Urick, Principles of Underwater Sound, 3rd ed., Ed. New York:
McGraw-Hill, 1983.
[2] R. F. Coates, Underwater Acoustic Systems. Ed. London: McMillan,
1990.
[3] 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.
[4] D. K. Mellinger, Ch W. Clark, "A method for filtering bioacoustic
transients by spectrogram image convolution," Oceans, 1993.
[5] 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.
[6] 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.
[7] 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.
[8] R. Backus, W. E. Schevill, "Physeter clics," Whales, Porpoises and
Dolphins, Ed. Univ. Calif. by K. S. Norris, pp. 510-28, 1966.
[9] W. Watkins, W. Schevill, "Sperm whale codas," J. Acoust. Soc. Am.,
vol. 62, 1977.
[10] B. Mohl, M. Wahlberg, P. T. Madsen, "Sperm Whale Clicks:
directionality and source level revisited," J. Acoust. Soc. Am., pp. 638-
48, 2000.
[11]R. Boileau, "Whale soundings," Zoological Physics, 438, 2002
[12] J. C. Goold, S. Jones, "Time and frequency domain characteristic of
sperm whale clicks," J. Acoust. Soc. Am., pp. 1279-91, 1996.
[13] 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.
[14] 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.
[15] W. Lauterborn, U. Parlitz, "Methods of chaos physics and their
applications to acoustic," J. Acoust. Soc. Am., vol 84, pp. 1975-93, 1988.
[16] H. Kantz, T. Schreiber, Nonlinear Times Series Analysis, Ed. Cambridge
PU, 1997.
[17] C. Tiemann, M. Porter, L. Frazer, "Automated Model-Based
Localization of Marine Mammals near Hawaï," in Proc. of Oceans 2001
Conference, Hawaï, 2001
[18] J. Ward, K. Fitspatrick, N. DiMarzio, "New algorithms for open ocean
marine mammal monitoring," in Proc. of Oceans Conference 2000.
[19] M. Lopatka, "Reconnaissance de signatures acoustiques pour la
distinction d'individus dans un groupe de cachalots,", iSnS report,
University Paris 12, France, 2002.
[20] S. Haykin, Signal Processing, Ed. IEEE Press, 1994.
[21] J. Max, Méthodes et techniques de traitement du signal et applications
aux mesures physiques, Paris: Ed. Masson, 1982.
[22] ID. Landau, Identification et commandes des systèmes, Paris: Ed.
Hermes, 1988.
[23] H. Akaïke, "A new look at the statistical model identification," IEEE
Trans. on Auto. Control, vol.6, pp. 716-23, 1974.
[24] Y. Meyer, Les ondelettes, Algorithmes et Applications, Paris: Ed.
Armand Colin, 1992.
[25] I. Daubechies, Ten Lectures on Wavelets, Philadelphia: Ed. Society for
Industrial and Applied Mathematics, 1992
[26] J. Herault, C. Jutten, Réseaux neuronaux et traitement du signal, Paris:
Ed. Hermes, 1994
[27] D.E. Rumelhart, G.E. Hinton, R.J. Williams, "Learning representations
by back-propagation errors," Nature, vol. 323, pp. 533-36, 1986.
[28] O. Adam, "Approche compare des techniques connexionnistes et
adaptatives pour le traitement des signaux lidar," Thesis, University
Paris VI, 1995.