Abstract
A study of detecting North Atlantic Right Whale (NARW) up-calls has been conducted with measurements from passive acoustic monitoring devices. Denoising and normalization algorithms are applied to remove local variance and narrowband noise in order to isolate the NARW up-calls in spectrograms. The resulting spectrograms, after binarization, are treated with a region detection procedure called the Moor-Neighbor algorithm to find continuous objects that are candidates of up-call contours. After selected properties of each detected object are computed, they are compared with a pair of low and high empirical thresholds to estimate the probability of the detected object being an up-call; therefore, those objects that are determined with certainty to be non up-calls are discarded. The final stage in the proposed call detection method is to separate true up-calls from the rest of potential up-calls with classifiers such as linear discriminate analysis (LDA), Naïve Bayes, and decision tree. Experimental results using the data set obtained by Cornell University show that the proposed method can achieve accuracy to 96%.
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