Automated detection and identification of blue and fin whale foraging calls by combining pattern recognition and machine learning techniques

a novel approach has been developed for detecting and classifying foraging calls of two mysticete species in passive acoustic recordings. This automated detector/classifier applies a computer-vision based technique, a pattern recognition method, to detect the foraging calls and remove ambient noise effects. The detected calls were then classified as blue whale D-calls [1] or fin whale 40-Hz calls [2] using a logistic regression classifier, a machine learning technique. The detector/classifier has been trained using the 2015 Detection, Classification, Localization and Density Estimation (DCLDE 2015, Scripps Institution of Oceanography UCSD [3]) low-frequency annotated set of passive acoustic data, collected in the Southern California Bight, and its out-of-sample performance was estimated by using a cross-validation technique. The DCLDE 2015 scoring tool was used to estimate the detector/classifier performance in a standardized way. The pattern recognition algorithm's out-of-sample performance was scored as 96.68% recall with 92.03 % precision. The machine learning algorithm's out-of-sample prediction accuracy was 95.20%. The result indicated the potential of this detector/classifier on real-time passive acoustic marine mammal monitoring and bioacoustics signal processing.