Classification of Ocean Acoustic Data Using AR Modeling and Wavelet Transforms

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 earthqua...

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Bibliographic Details
Main Authors: Fargues, Monique P., Bennett, R., Barsanti, R.J.
Other Authors: Electrical and Computer Engineering (ECE), Graduate School of Engineering and Applied Science (GSEAS), Electrical and Computer Engineering
Format: Report
Language:unknown
Published: Monterey, California. Naval Postgraduate School 1997
Subjects:
wavelet transform
AR modeling
classification
Humpback Whale
Killer Whale
Sperm whale
Killer whale
Online Access:https://hdl.handle.net/10945/15299
Description
Summary: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% Approved for public release; distribution is unlimited. N0002495WR10820 Naval Undersea Warfare Center, Newport Division

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