Classification of Ocean Acoustic Data Using AR Modeling and Wavelet Transforms

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Authors
Fargues, Monique P.
Bennett, R.
Barsanti, R.J.
Subjects
wavelet transform, AR modeling, classification
Advisors
Date of Issue
1997-01
Date
December 1994 - June 1996
Publisher
Monterey, California. Naval Postgraduate School
Language
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%
Type
Technical Report
Description
Series/Report No
Department
Electrical and Computer Engineering
Identifiers
NPS Report Number
NPS-EC-97-001
Sponsors
Naval Undersea Warfare Center, Newport Division
Funder
N0002495WR10820
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
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