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, M. P., Bennett, R., Barsanti, R. J.
Other Authors: NAVAL POSTGRADUATE SCHOOL MONTEREY CA DEPT OF ELECTRICAL AND COMPUTER ENGINEE RING
Format: Text
Language:English
Published: 1997
Subjects:
Acoustic Detection and Detectors
*ACOUSTIC DATA
*UNDERWATER SOUND SIGNALS
REGRESSION ANALYSIS
OCEANOGRAPHIC DATA
CLASSIFICATION
EARTHQUAKES
MARINE BIOLOGICAL NOISE
WAVELET TRANSFORMS
AUTOREGRESSION
Humpback Whale
Killer Whale
Sperm whale
Killer whale
Online Access:http://www.dtic.mil/docs/citations/ADA321626
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA321626
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%.

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