ABSTRACT
This paper presents recent experimental results and a discussion of system enhancements made to the real-time autonomous humpback whale detector-classifier algorithm first presented by Abbot et al. [J. Acoust. Soc. Am. 127, 2894-2903 (2010)]. In February 2010, a second-generation system was deployed in an experiment conducted off of leeward Kauai during which 26 h of humpback vocalizations were recorded via sonobuoy and processed in real time. These data have been analyzed along with 40 h of humpbacks-absent data collected from the same location during July-August 2009. The extensive whales-absent data set in particular has enabled the quantification of system false alarm rates and the measurement of receiver operating characteristic curves. The performance impact of three enhancements incorporated into the second-generation system are discussed, including (1) a method to eliminate redundancy in the kernel library, (2) increased use of contextual analysis, and (3) the augmentation of the training data with more recent humpback vocalizations. It will be shown that the performance of the real-time system was improved to yield a probability of correct classification of 0.93 and a probability of false alarm of 0.004 over the 66 h of independent test data.
