Detection and Classification of Underwater Acoustic Transients Using Neural Networks

Thomas L. Hemminger, Yoh Han Pao

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


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 named for the German mathematician, Hausdorff. 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 below.

Original languageEnglish (US)
Pages (from-to)712-718
Number of pages7
JournalIEEE Transactions on Neural Networks
Issue number5
StatePublished - Sep 1994

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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