Automatic Classification of Cetacean Vocalizations Using an Aural Classifier

Abstract

LONG-TERM GOALS: To develop a robust automatic classifier with a high probability of detection and a low false alarm rate that can classify vocalizations from a variety of cetacean species. In this research, we wish to apply a unique automatic classifier developed by the PI that uses perceptual signal features - features similar to those employed by the human auditory system to classify cetacean species vocalizations and reject anthropogenic false alarms. This aural classifier has been successfully used to distinguish between active-sonar echoes from man-made (i.e. metallic) structures and naturally occurring clutter sources [1, 2] and performs as well or better than expert sonar operators [3]. Many of the features were inspired by research directed at discriminating the timbre of different musical instruments a passive classification problem which suggests it should be able to classify marine mammal vocalizations since these calls possess many of the acoustic attributes of music.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2012
Accession Number
ADA573485

Entities

People

  • Carolyn M. Binder
  • Paul C. Hines

Organizations

  • Defence Research and Development Canada

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Acoustics
  • Algorithms
  • Automatic
  • Cetaceans
  • Classification
  • Data Sets
  • Detection
  • Detectors
  • False Alarms
  • Frequency
  • Machine Learning
  • Mammals
  • Marine Mammals
  • Odontocetes
  • Vocalization
  • Warning Systems
  • Whales

Readers

  • Marine Mammal Biology
  • Neural Network Machine Learning.
  • Speech Processing/Speech Recognition.