Sparse Representation Classification of Dolphin Whistles Using Gabor Wavelets

This research presents a novel approach to categorize dolphin whistles into various types. Most accurate methods to identify dolphin whistles are tedious and not robust, especially in the presence of ocean noise. One of the biggest challenges of dolphin whistle extraction is the coexistence of short-time duration wide-band echo clicks with the whistles. In this research, a subspace of select orientation parameters of the 2D Gabor wavelet frames is utilized to enhance or suppress signals by their orientation. The result is a Gabor image that contains a noise free grayscale representation of the fundamental dolphin whistle which is resampled and fed into the Sparse Representation Classifier. The classifier uses the l1 norm to select a match. Experimental studies conducted demonstrate: a a robust technique based on the Gabor wavelet filters in extracting reliable call patterns, and b the superior performance of Sparse Representation Classifier for identifying dolphin whistles by their call type.