Classification of Ocean Acoustic Data Using AR Modeling and Wavelet Transforms.

Abstract

This study investigates the application of orthogonal, non-orthogonal wavelet-based procedures, and AR modeling as feature extraction techniques to classify several classes of underwater signals consisting of sperm whale, killer whale, gray whale, pilot whale, humpback whale, and underwater earthquake data. A two-hidden-layer back-propagation neural network is used for the classification procedure. Performance obtained using the two wavelet-based schemes are compared with those obtained using reduced-rank AR modeling tools. Results show that the non-orthogonal undecimated A-trous implementation with multiple voices leads to the highest classification rate of 96.7%.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1997
Accession Number
ADA321626

Entities

People

  • M. P. Fargues
  • R. J. Barsanti
  • Robert K. A. Bennett

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Computers
  • Data Science
  • Engineering
  • Feature Extraction
  • Frequency
  • Frequency Domain
  • Frequency Response
  • Image Processing
  • Information Science
  • Machine Learning
  • Neural Networks
  • Odontocetes
  • Signal Processing
  • Time Domain
  • Wavelet Transforms

Readers

  • Marine Mammal Biology
  • Neural Network Machine Learning.
  • Radio communications and signal processing.

Technology Areas

  • AI & ML