Detection and classification of North Atlantic right whales in the bay of fundy using independent component analysis

Brian R. La Cour, Michael A. Linford

Abstract


A novel method of detection and classification for marine mammals is presented which uses techniques from independent component analysis to solve the blind source separation problem for North Atlantic right whales (Eubalaena glacialis). Using the fundamentally non-Gaussian nature of marine mammal vocalizations and data collected on multiple hydrophones, we are able to separate right whale source spectra, up to an unknown scale, from ambient noise. This technique assumes that the array data is a linear combination of non-Gaussian source signals but does not require specific knowledge of the array geometry. A detection algorithm which separates right whale vocalizations from ambient background using a Kolmogorov-Smirnov test statistic is presented and tested on data collected in the Bay of Fundy. The performance of the detector was found to be such that it was possible to achieve a probability of detection of about three-fourths with a false alarm probability of about one-third. Independent component analysis was found to provide little improvement over standard principle component analysis, which was used as preprocessing step.

Keywords


Algorithms; Automation; Data acquisition; Fish detectors; Gaussian noise (electronic); Hydrophones; Independent component analysis; Parameter estimation; Probability; Problem solving; Real time systems; Submarines; Acoustic automated detection; Multiple sound sources; Non-Gaussian signals; Vocalization

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