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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 -norm optimization procedure. Experimental studies conducted in this research reveal the advantages and limitations of the proposed method against some existing techniques such as -Nearest Neighbors and Support Vector Machines in distinguishing different vocalizations.


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