Detection and Recognition of North Atlantic Right Whale Contact Calls in the Presence of Ambient Noise

In this paper, the problem of detecting and recognizing North Atlantic right whale (NARW), Eubalaena glacialis, contact calls in the presence of ambient noise is considered. A proposed solution is based on a multistage, hypothesis-testing technique that involves the generalized likelihood ratio test (GLRT) detector, spectrogram testing, and feature vector testing algorithms. The main contributions of this paper are the inclusion of noise kernels for signals likely to produce false alarms and a second stage classification algorithm which extracts parameters from candidate contact calls and constructs a scaled squared error statistic for parameters which lie outside the range of expected calls. Closed-form representations of the algorithms are derived and realizable detection schemes are developed. Test results show that the proposed technique is able to detect approximately 80% of the contact calls detected by the human operator with about 26 false alarms per 24 h of observation. Testing data set included 44 227 right whale contact calls detected by eight human operators who performed visual and aural inspection of the data spectrogram. Data were collected in different periods from March 2001 to February 2007, in Cape Cod Bay, Great South Channel, and in the coastal waters of Georgia.