title: Detection and classification of underwater acoustic transients using neural networks.
abstract: Underwater acoustic transients can develop from a wide variety of sources. Accordingly, detection and classification of such transients by automated means can be exceedingly difficult. This paper describes a new approach to this problem based on adaptive pattern recognition employing neural networks and an alternative metric, the Hausdorff metric. The system uses self-organization to both generalize and provide rapid throughput while utilizing supervised learning for decision making, being based on a concept that temporally partitions acoustic transient signals, and as a result, studies their trajectories through power spectral density space. This method has exhibited encouraging results for a large set of simulated underwater transients contained in both quiet and noisy ocean environments, and requires from five to ten MFLOPS for the implementation described.
authors.affiliation: Dept. of Eng. and Eng. Technol., Pennsylvania Univ., Erie, PA.
authors.forename: T L
authors.initials: TL
authors.lastname: Hemminger
authors.forename: Y H
authors.initials: YH
authors.lastname: Pao