Automated Classification of Beaked Whales and Other Small Odontocetes in the Tongue of the Ocean, Bahamas

Abstract

Navy sonar has recently been associated with a number of marine mammal stranding events. Beaked whales have been the predominant species involved in a number of these strandings. Monitoring and mitigating the effects of anthropogenic noise on marine mammals are active areas of research. Key to both monitoring and mitigation is the ability to automatically detect and classify the animals, especially beaked whales. This paper presents a novel support vector machine based methodology for automated species level classification of small odontocetes. To date, the algorithm presented has been trained to differentiate the click vocalizations of Blainville's beaked whales (Mesoplodon densirostris) from the clicks produced by delphinids and from man-made sounds. The automated classification capability compliments the detection and tracking tools already developed through ONR funding for the monitoring and localization of whales at the Atlantic Undersea Test and Evaluation Center, Andros Island, Bahamas.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2006
Accession Number
ADA497373

Entities

People

  • David Morretti
  • Nancy Dimarzio
  • Ronald Morrissey
  • Susan Jarvis

Organizations

  • Naval Undersea Warfare Center

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Ambient Noise
  • Animals
  • Classification
  • Doppler Effect
  • Machine Learning
  • Mammals
  • Marine Mammals
  • Noise
  • Oceans
  • Odontocetes
  • Random Variables
  • Supervised Machine Learning
  • Test And Evaluation
  • Tongue Of The Ocean
  • Two Dimensional

Fields of Study

  • Environmental science

Readers

  • Computational Modeling and Simulation
  • Marine Mammal Biology

Technology Areas

  • AI & ML