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Using local binary patterns as features for classification of dolphin calls
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1. Agranat, I. (2009). Automatically Identifying Animal Species from Their Vocalizations (Wildlife Acoustics, Inc., Concord, MA), pp. 122.
2. Ahohen, T. , Hadid, A. , and Pietikäinen, M. (2004). “Face recognition with local binary patterns,” in 8th European Conference on Computer Vision, 469481.
3. Buck, J. R. , and Tyack, P. L. (1993). “A quantitative measure of similarity for Tursiops truncatus signiture whistles,” J. Acoust. Soc. Am. 94, 24972506.
4. Datta, S. , and Sturtivant, C. (2002). “Dolphin whistle classification for determining group identities,” Signal Process. 82, 251258.
5. Devijner, P. , and Kittler, J. (1982). Pattern Recognition: A Statistical Approach (Prentice-Hall, Englewood Cliffs, NJ).
6. Dugan, P. J. , Rice, A. N. , Urazghildiiev, I. R. , and Clark, C. W. (2010). “North Atlantic right whale acoustic signal processing: Part I. comparison of machine learning recognition algorithms,” in IEEE Long Island Systems, Applications and Technology Conference, pp. 16.
10. Gannier, A. , Fuchs, S. , Quèbre, P. , and Oswald, J. N. (2010). “Performance of a contour-based classification method for whistles of Mediterranean delphinids,” Appl. Acoust. 71, 10631069.
11. Gillespie, D. (2004). “Detection and classification of right whale calls using an edge detector operating on a smoothed spectrogram,” J. Can. Acoust. Assoc. 32, 3947.
7. Halkias, X. , and Ellis, D. P. W. (2006). “Call detection and extraction using Bayesian inference,” Appl. Acoust. 67, 11641174.
8. Huang, C.-L. , and Huang, D.-H. (1998). “A content-based image retrieval system,” Image Vision Comput. 16, 149163.
9. Huang, D. , and Shan, C. (2011). “Local binary patterns and its applications to facial image analysis: A survey,” IEEE Trans. Systems Man Cybernetics 41, 765781.
12. Mallawaarachchi, A. , and Ong, S. H. (2008). “Spectrogram denoising and automated extraction of the fundamental frequency variation of dolphin whistles,” J. Acoust. Soc. Am. 124, 11591170.
13. Mazhar, S. , and Ura, T. (2007). “Vocalization based individual classification of humpback whales using support vector machine,” in OCEANS 2007, pp. 19.
14. McCowan, B. (1995). “A new quantitative technique for categorizing whistles using simulated signals and whistles from captive bottlenose dolphins (Delphinidae, Tursiopstruncatus),” Ethology 100, 177193.
15. Mohammad, B. , and McHugh, R. (2011). “Automatic detection and characterization of dispersive north Atlantic right whale upcalls recorded in a shallow-water environment using a region-based active contour model,” IEEE J. Oceanic Eng. 36, 431440.
16. Mouy, X. , Leary, D. , Martin, B. , and Laurinolli, M. (2008). “A comparison of methods for the automatic classification of marine mammal vocalizations in the Arctic,” in New Trends for Environmental Monitoring Using Passive Systems, pp. 16.
17. Nanayakkara, S. C. , Chitre, M. , Ong, S. H. , and Taylor, E. (2007). “Automatic classification of whistles produced by Indo-Pacific humpback dolphins,” in OCEANS 2007, pp. 15.
18. Ojala, T. , Pietikäinen, M. , and Harwood, D. (1996). “A comparative study of texture measures with classification based on featured distribution,” Pattern Recognit. 29, 5159.
19. Ojala, T. , Pietikäinen, M. , and Mäenpää, T. (2002). “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 971987.
20. Pace, F. , Benard, F. , Glotin, H. , Adam, O. , and White, P. (2010). “Subunit definition and analysis for humpback whale call classification,” Appl. Acoust. 71, 11071112.
21. Seekings, P. J. , Yeo, K. P. , Chen, Z. P. , Nanayakkara, S. C. , Tan, J. , Tay, P. , and Taylor, E. (2010). “Classification of a large collection of whistles from indo-pacific humpback dolphins (Sousa chinensis),” in OCEANS 2010, pp. 15.

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An image processing technique called Local Binary Patterns (LBP) has been explored for its ability to generate feature vectors for dolphin vocalization classification. The LBP operator eliminates the need for contour tracing, denoising, and other prior processing. In an experimental study of classifying dolphin whistle types, the performance of the LBP operation was compared with that of the popular contour-based Time-Frequency Parameters (TFP) approach. The preliminary experimental results illustrate that the LBP method produces more consistent classifier accuracy of dolphin whistle calls even when the contour shapes are complex and populated with impulsive clicks and anthropogenic harmonics.


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Scitation: Using local binary patterns as features for classification of dolphin calls