Comparison of machine learning techniques for the classification of echolocation clicks from three species of odontocetes

Marie A. Roch, Melissa S. Soldevilla, Rhonda Hoenigman, Sean M. Wiggins, John A. Hildebrand

Abstract


A species classifier is presented which decides whether or not short groups of clicks are produced by one or more individuals from the following species: Blainville's beaked whales, short-finned pilot whales, and Risso's dolphins. The system locates individual clicks using the Teager energy operator and then constructs feature vectors for these clicks using cepstral analysis. Two different types of detectors confirm or reject the presence of each species. Gaussian mixture models (GMMs) are used to model time series independent characteristics of the species feature vector distributions. Support vector machines (SVMs) are used to model the boundaries between each species' feature distribution and that of other species. Detection error tradeoff curves for all three species are shown with the following equal error rates: Blainville's beaked whales (GMM 3.32%/SVM 5.54%), pilot whales (GMM 16.18%/SVM 15.00%), and Risso's dolphins (GMM 0.03%/SVM 0.70%).

Keywords


Artificial intelligence; Classification (of information); Communication channels (information theory); Computer networks; Database systems; Dolphins (structures); Error analysis; Error detection; Face recognition; Image retrieval; Learning algorithms; Learning systems; Magnetostrictive devices; Time series analysis; Cepstral analysis; Echolocation clicks; Feature vectors; Odontocetes; Teager energy operator

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