No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
Sparse representation for classification of dolphin whistles by type
1. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “ Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2008).
2. R. Min and J.–L. Dugelay, “ Improved combination of LBP and sparse representation based classification (SRC) for face recognition,” IEEE International Conference on Multimedia and Expo (ICME), July 1–6, 2011.
3. X. Chen and P. J. Ramadge, “ Music genre classification using multiscale scattering and sparse representations,” 47th Annual Conference on Information Sciences and Systems (CISS), March 1–6, 2013.
4. S. Zubair and W. Wang, “ Audio classification based on sparse coefficients,” Sensor Signal Processing for Defense, September 1–5, 2011.
5. C. M. Fira, L. Goras, C. Barabasa, and N. Cleju, “ ECG compressed Sensing based on classification in compressed space and specified dictionaries,” 19th European Signal Processing Conference (EUSIPCO 20100), Spain, September (2011).
6. L. N. Tan, G. Kossan, M. L. Cody, C. E. Taylor, and A. Alwan, “ A sparse representation-based classifier for in-set bird phrase verification and classification with limited training data,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May (2013), pp. 763–767.
7. M. Esfahanian, H. Zhuang, and N. Erdol, “ Using local binary patterns as features for classification of dolphin calls,” J. Acoust. Soc. Am. 134, EL105–EL111 (2013).
8. B. Mohamamd and R. McHugh, “ 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. Ocean. Eng. 36, 431–440 (2011).
9. J. R. Buck and P. L. Tyack, “ A quantitative measure of similarity for Tursiops truncatus signature whistles,” J. Acoust. Soc. Am. 94, 2497–2506 (1993).
10. A. Gannier, S. Fuchs, P. Quèbre, and J. N. Oswald, “ Performance of a contour-based classification method for whistles of Mediterranean delphinids,” Appl. Acoust. 71, 1063–1069 (2010).
11. H. Ou, W. W. L. Au, L. M. Zurk, and M. O. Lammers, “ Automated extraction and classification of time-frequency contours in humpback vocalizations,” J. Acoust. Soc. Am. 133, 301–310 (2013).
12. R. S. Wells, “ The role of long-term study in understanding the social structure of a bottlenose dolphin community,” in Dolphin Societies: Discoveries and Puzzles, edited by K. Pryor and K. S. Norris (University of California Press, Berkeley, CA, 1991), pp. 199–225.
13. R. S. Wells, “ Dolphin social complexity: Lessons from long-term study and life history,” in Animal Social Complexity: Intelligence, Culture, and Individualized Societies, edited by F. B. M. deWaal and P. L. Tyack (Harvard University Press, Cambridge, MA, 2003), pp. 32–56.
14. L. S. Sayigh and V. M. Janik, “ Signature whistles,” in Encyclopedia of Marine Mammals, edited by W. F. Perrin, B. Würsig, and J. G. M. Thewissen (Academic Press, London, 2009), pp. 1014–1016.
16. J. N. Oswald, S. Rankin, J. Barlow, and M. O. Lammers, “ A tool for real-time acoustic species identification of delphinid whistles,” J. Acoust. Soc. Am. 122, 587–595 (2007).
Article metrics loading...
A compressive-sensing approach called Sparse Representation Classifier (SRC) is applied to the classification of bottlenose dolphin whistles by type. The SRC algorithm constructs a dictionary of whistles from the collection of training whistles. In the classification phase, an unknown whistle is represented sparsely by a linear combination of the training whistles and then the call class can be determined with an l 1-norm optimization procedure. Experimental studies conducted in this research reveal the advantages and limitations of the proposed method against some existing techniques such as K-Nearest Neighbors and Support Vector Machines in distinguishing different vocalizations.
Full text loading...
Most read this month