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Passive acoustic monitoring of blue whales (Balaenoptera musculus) has been used to gain insight into their presence and behavior. Many of their calls have been shown to be detectable through spectrogram correlation due to the low variation in these stereotyped calls. In this work, we describe rule based classifiers for tonal B and D calls using the pitch/frequency contour information obtained from a contour extractor. B calls can be detected by spectrogram correlation, but the D calls are highly variable and are therefore difficult to detect using spectrogram kernel methods. Experiments on four hours of evaluation data from different field seasons show that 91.3% of B calls and 85.8% of D calls were correctly retrieved. For both types of calls, less than 2% of the retrieved calls were false positives.

The sea is home to a myriad of marine animal species, many of which use sound as a primary means of communication, navigation and foraging. Of particular interest are the Blue whales (Balaenoptera musculus) of the cetacean family. Massive commercial whaling prior to 1960 brought the species close to extinction and its population still remains very low. Passive acoustic monitoring of baleen whales has recently been used to provide long-term information about their presence and behavior, and provides an attractive complement to traditional visual based monitoring. In this work we present a frequency domain based algorithm developed for extracting the frequency contours of the dominant harmonic in tonal calls of blue whales (B and D calls). The algorithm uses a two pass approach to contour extraction. In the first pass, partial candidate contours are formed, followed by a second pass which uses the partial information to construct complete contours. When evaluated on a one hour labeled recording, the algorithm had 90% recall and 76% precision.