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A novel image-processing based method for the automatic detection, extraction and characterization of marine mammal tonal calls

Published online by Cambridge University Press:  09 July 2009

Antonio Sánchez-García ,
Patricio Muñoz-Esparza  and
José Luis Sancho-Gomez
Antonio Sánchez-García*
Affiliation:
Sociedad Anónima de Electrónica Submarina (SAES), Carretera de la Algameca s/n, 30205-Cartagena (Spain)
Patricio Muñoz-Esparza
Affiliation:
Sociedad Anónima de Electrónica Submarina (SAES), Carretera de la Algameca s/n, 30205-Cartagena (Spain)
José Luis Sancho-Gomez
Affiliation:
Universidad Politécnica de Cartagena, Departamento de Tecnologías de la Información y las Comunicaciones, Campus Muralla del Mar s/n, 30202-Cartagena (Spain)
*
Correspondence should be addressed to: A. Sánchez-García, Sociedad Anónima de Electrónica Submarina (SAES), Carretera de la Algameca s/n, 30205-Cartagena (Spain) email: [email protected]
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Abstract

A novel, automatic method for the detection, extraction and characterization of marine mammal tonal calls is presented. Signals are automatically detected from the spectrogram, isolated using region-based segmentation, extracted and finally characterized by means of a fixed number of radial basis function (RBF) coefficients. A total of sixteen RBF coefficients are sufficient to accurately capture the time–frequency information contained in the calls. These coefficients can be later used to classify signals based on their characteristics. New specific functions for contour extraction and cross-resolution have been developed. The performance of the method has been extensively tested using simulated signals and a set of recordings covering a significant range of situations that can be encountered at sea.

Keywords

detectionextractioncharacterizationmarine mammal callregion-based segmentationradial basis functionscontour extractioncross resolution
Type
Research Article
Information
Journal of the Marine Biological Association of the United Kingdom , Volume 90 , Special Issue 8: Marine Mammals , December 2010 , pp. 1667 - 1684
Copyright
Copyright © Marine Biological Association of the United Kingdom 2009

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References

REFERENCES

Adam, O. (2006) Advantages of the Hilbert Huang transform for marine mammals signal analysis. Journal of the Acoustical Society of America 120, 2965–2973.CrossRefGoogle Scholar
Au, W.W.L. (1993) The sonar of dolphins. 1st edition. New York: Springer-Verlag.CrossRefGoogle Scholar
Bazua-Duran, C. and Au, W.W.L. (2002) The whistles of Hawaiian spinner dolphin. Journal of the Acoustical Society of America 112, 3064–3072.CrossRefGoogle Scholar
Brandes, T.S., Naskrecki, P. and Figueroa, H.K. (2004) Using image processing to detect and classify narrow-band cricket and frog calls. Journal of the Acoustical Society of America 120, 2950–2957.CrossRefGoogle Scholar
Brown, J.C. and Miller, P.J.O. (2006) Automatic classification of killer whale vocalizations using dynamic time warping. Journal of the Acoustical Society of America 122, 1201–1207.CrossRefGoogle Scholar
Buck, J.R. and Tyack, P.L. (1993) A quantitative measure of similarity for Tursiops truncatus signature whistles. Journal of the Acoustical Society of America 94, 2497–2506.CrossRefGoogle ScholarPubMed
Caldwell, M.C., Caldwell, D.K. and Tyack, P.L. (1990) A review of the signature whistle hypothesis for the Atlantic bottlenose dolphin. In Leatherwood, S. and Reeves, R.R. (eds) The bottlenose dolphin. San Diego, CA: Academic Press, Inc, pp. 199–234.CrossRefGoogle Scholar
Chen, Z. and Maher, R.C. (2006) Semi-automatic classification of bird vocalization using spectral peak tracks. Journal of the Acoustical Society of America 120, 2974–2984.CrossRefGoogle ScholarPubMed
Datta, S. and Sturtivant, C. (2002) Dolphin whistle classification for determining group identities. Signal Processing 82, 251–258.CrossRefGoogle Scholar
Deecke, V.B. and Janik, V.M. (2006) Automated categorization of bioacoustic signals. Avoiding perceptual pitfalls. Journal of the Acoustical Society of America 119, 645–653.CrossRefGoogle ScholarPubMed
Deecke, V.B., Ford, J.K.B. and Spong, P. (1999) Quantifying complex patterns of bioacoustic variation: use of a neural network to compare killer whale (Orcinus orca) dialects. Journal of the Acoustical Society of America 105, 2499–2507.CrossRefGoogle Scholar
Ghosh, J., Deuser, L.M. and Beek, S.D. (1992) A neural network based hybrid system for detection, characterization and classification of short-duration oceanic signals. IEEE Journal of Oceanic Engineering 17, 351–363.CrossRefGoogle Scholar
Gillespie, D. (2004) Detection and classification of right whale calls using an ‘edge’ detector operating on a smoothed spectrogram. Canadian Acoustics 32, 39–47.Google Scholar
Gonzalez, R.C. and Woods, R.E. (2001) Digital image processing. 2nd edition. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
Gonzalez, R.C., Woods, R.E. and Eddins, S.L. (2004) Digital image processing using MATLAB. 1st edition. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
Gordon, J.C.D., Gillespie, D., Potter, J., Frantzis, A., Simmonds, M., Swift, R. and Thompson, D. (2004) A review of the effects of seismic survey on marine mammals. Marine Technology Society Journal 37, 14–34.Google Scholar
Halkias, C.H. and Ellis, D.P.W. (2006) Call detection and extraction using Bayesian inference. Applied Acoustics 67, 1164–1174.CrossRefGoogle Scholar
Haykin, S. (1999) Neural networks. A comprehensive foundation. 2nd edition. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
Hu, Y.H. and Hwang, J.N. (eds) (2002) Handbook of neural network signal processing. 1st edition. Boca Raton, FL: CRC Press.Google Scholar
Janik, V.M. (1999) Pitfalls in the categorization of behaviour: a comparison of dolphin whistle classification methods. Animal Behaviour 57, 133–143.CrossRefGoogle ScholarPubMed
Janik, V.M. and Slater, P.J.B. (1998) Context specific use suggests that bottlenose dolphin signature whistles are cohesion calls. Animal Behaviour 56, 829–838.CrossRefGoogle ScholarPubMed
Lammers, M.O., Au, W.W.L. and Herzing, D.L. (2003) The broadband social acoustic signalling behaviour of spinner and spotted dolphins. Journal of the Acoustical Society of America 114, 1629–1639.CrossRefGoogle ScholarPubMed
Learned, R.E. and Willsky, A.S. (1995) A wavelet packet approach to transient signal classification. Applied and Computational Harmonic Analysis 2, 265–278.CrossRefGoogle Scholar
Matej, S. and Lewitt, R.M. (1996) Practical considerations for 3-D image reconstruction using spherically symmetric volume elements. IEEE Transactions on Medical Imaging 15, 68–78.CrossRefGoogle ScholarPubMed
McCowan, B. and Reiss, D. (2001) The fallacy of ‘signature whistles’ in bottlenose dolphins: a comparative perspective of ‘signature information’ in animal vocalizations. Animal Behaviour 62, 1151–1162.CrossRefGoogle Scholar
Mellinger, D.K. and Clark, C.W. (2000) Recognizing transient low-frequency whale sounds by spectrogram correlation. Journal of the Acoustical Society of America 107, 3518–3529.CrossRefGoogle ScholarPubMed
Mellinger, D.K. and Barlow, J. (2003) Future directions for acoustic marine mammal surveys: stock assessment and habitat use. NOOA OOR Special Report. NOOA/PMEL Contribution 2557. 37 pp.Google Scholar
Murray, S.O., Mercado, E. and Roitblat, H. (1998) The neural network classification of false killer whale (Pseudorca crassidens) vocalizations. Journal of the Acoustical Society of America 104, 3626–3633.CrossRefGoogle ScholarPubMed
Norris, T.F., McDonald, M. and Barlow, J. (1999) Acoustic detection of singing humpback whales (Megaptera novaeangliae) in the eastern North Pacific during their northbound migration. Journal of the Acoustical Society of America 106, 506–514.CrossRefGoogle ScholarPubMed
Oswald, J.N., Rankin, S. and Barlow, J. (2004) The effect of recording and analysis bandwidth on acoustic identification of delphinid species. Journal of the Acoustical Society of America 116, 3178–3185.CrossRefGoogle ScholarPubMed
Oswald, J.N., Rankin, S., Barlow, J. and Lammers, M.O. (2007) A tool for real-time acoustic species identification of delphinid whistles. Journal of the Acoustical Society of America 122, 587–595.CrossRefGoogle ScholarPubMed
Park, J. and Sandberg, I.W. (1991) Universal approximation using radial-basis-function networks. Neural Computation 3, 246–257.CrossRefGoogle ScholarPubMed
Park, J. and Sandberg, I.W. (1993) Approximation and radial-basis-function networks. Neural Computation 5, 305–316.CrossRefGoogle Scholar
Pavan, G. (2007) Acoustic Risk Mitigation in the Mediterranean Sea. Current situation and recommendations. In Proceedings of the Underwater Defence Technology (UDT), Naples (Italy).Google Scholar
Popper, A.N. (1980) Sound emission and detection by delphinids. In Herman, L.M. (ed.) Cetacean behavior: mechanisms and function. New York: John Wiley & Sons, pp. 1–52.Google Scholar
Richardson, W.J., Green, C.R., Malme, C.I. and Thomson, D.H. (1995) Marine mammals and noise. San Diego, CA: Academic Press Inc.Google Scholar
Rickwood, P. and Taylor, A. (2008) Methods for automatically analyzing humpback songs units. Journal of the Acoustical Society of America 123, 1763–1772.CrossRefGoogle Scholar
Roch, M.A., Soldevilla, M.S., Burtenshaw, J.C., Henderson, E.E. and Hildebrand, J.A. (2007) Gaussian mixture model classification of odontocetes in the Southern California Bight and the Gulf of California. Journal of the Acoustical Society of America 121, 1737–1748.CrossRefGoogle ScholarPubMed
Sanchez-Garcia, A., Rodrigo, F.J. and Sancho-Gómez, J.L. (2008) Sonar transient false alarm reduction based on automated detection and characterization of marine mammal sounds. In Proceedings of the Underwater Defence Technology (UDT), Glasgow (UK).Google Scholar
Sanner, R.M. and Slotine, J.E. (1994) Gaussian network for direct adaptive control. IEEE Transactions on Neural Network 3, 837–863.CrossRefGoogle Scholar
Searby, A. and Jouventin, P. (1995) The double vocal signature of crested penguins: is the identity coding system of rockhoppers (Eudyptes chrysocome) due to phylogeny or ecology? Journal of Avian Biology 36, 449–460.CrossRefGoogle Scholar
Simmonds, M.P. and Lopez-Jurado, L.F. (1991) Whales and the military. Nature 351, 448.CrossRefGoogle Scholar
Theriault, J.A. (2005) Marine mammals and active sonar. Sea Technology November, 23–29.Google Scholar
Trifa, V.M., Kirschel, A.N.G., Taylor, C.E. and Vallejo, E.E. (2008) Automated species recognition of antbirds in a Mexican rainforest using hidden Markov models. Journal of the Acoustical Society of America 123, 2424–2431.CrossRefGoogle Scholar
Tyack, P.L. and Clark, C.W. (2000) Communication and acoustic behaviour of dolphin and whales. In Au, W.W.L., Popper, A.N. and Fay, R.R. (eds) Hearing by whales and dolphins. New York: Springer Verlag, pp. 156–224.CrossRefGoogle Scholar
Van Ijsselmuide, S.P. and Beerens, S.P. (2004) Detection and classification of marine mammals using an LFAS system. Canadian Acoustics 32, 93–106.Google Scholar