Automatic Detection and Characterization of Dispersive North Atlantic Right Whale Upcalls Recorded in a Shallow-Water Environment Using a Region-Based Active Contour Model

Abstract

Various automatic detection and characterization techniques have been proposed to implement mitigation measures to protect the endangered North Atlantic Right Whales in their habitats. These species prefer shallow waters during their seasonal migration. Such shallow-water acoustical environments act as a waveguide and cause the monocomponent upcall emitted by the vocalizing whale to become a dispersive multicomponent signal with different time arrivals and relative energy. In this paper, the discrimination between the Right Whale upcalls and background noise has been investigated using the support vector machine classifier. To perform this task, a region-based active contour segmentation method is proposed. In this work, both synthesized data based on typical Right Whale vocalizations and real data recorded in Cape Cod Bay are used to evaluate the proposed method. We show how the shallow-water dispersion effects which cause higher order mode generation affect the parameters used for classification and descriptive statistics. We compare the descriptive statistics of the call duration using both single-mode and multimode approaches. The single-mode analysis was performed by extracting the frequency contour of the first mode.