The Application of Artificial-Intelligence Techniques to the Automatic Identification of Sounds Received at Hydrophones and to the Correlation of These Sounds Between Hydrophones

The U. S. Navy's Integrated Underwater Surveillance System (IUSS) monitors hydrophones in the northeast Pacific. Geophysicists studying the IUSS data find seismic events and correlate them between hydrophones to locate their sources. Marine biologists and intelligence personnel are interested i...

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Bibliographic Details
Main Author: Seem, Dennis A.
Other Authors: NAVAL POSTGRADUATE SCHOOL MONTEREY CA
Format: Text
Language:English
Published: 1993
Subjects:
Computer Programming and Software
Acoustic Detection and Detectors
*MONITORING
*ARTIFICIAL INTELLIGENCE
*MONITORS
*UNDERWATER SOUND
*HYDROPHONES
*COMPUTER VISION
*ACOUSTIC TRACKING
*UNDERSEA SURVEILLANCE
SHAPE
RATES
THESES
SEISMIC DETECTION
FAR FIELD
FAST FOURIER TRANSFORMS
BIOLOGISTS
SCIENTISTS
WHALES
UNDERWATER
NORTH PACIFIC OCEAN
LOW FREQUENCY
DISTORTION
ARCHITECTURE
GEOPHYSICS
PERSONNEL
TIME
IDENTIFICATION
CORRELATION
SOUND
COMPUTER GRAPHICS
WORK STATIONS
*UNDERWATER SOUND LOCATORS
IUSS(INTEGRATED UNDERWATER SURVEILLANCE SYSTEM)
BALEEN WHALES
WHALE MOANS
Pacific
baleen whales
Online Access:http://www.dtic.mil/docs/citations/ADA276736
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA276736
Description
Summary:The U. S. Navy's Integrated Underwater Surveillance System (IUSS) monitors hydrophones in the northeast Pacific. Geophysicists studying the IUSS data find seismic events and correlate them between hydrophones to locate their sources. Marine biologists and intelligence personnel are interested in the identification and localization of other sounds in the IUSS data. The current means of identifying and correlating sounds is a laborious visual examination of the data on a graphics workstation. In this thesis, a computer-vision method is presented for automatically identifying the sources of low-frequency sounds that are received on the IUSS hydrophones. Also presented in this thesis is a blackboard architecture for correlating sound 'shapes' between hydrophones using a time-shift transform. The methods in this thesis properly identify approximately 90% of apparent whale moans and 100% of seismic events. The use of the time-shift transform has resulted in nearly a 100% success rate in correlating whale moans between far-field hydrophones, despite marked sound distortions with distance. Sound correlation, Sound identification, Artificial intelligence, Blackboard architecture.

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