On using back propagation neural networks to separate single echoes from multiple echoes

  • Authors:

  • W. Chang;B. Bosworth;G. Clifford Carter

  • Affiliations:

  • NUWC Detachment, New London, New London, Connecticut;NUWC Detachment, New London, New London, Connecticut;NUWC Detachment, New London, New London, Connecticut

  • Venue:
  • ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
  • Year:
  • 1993

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Abstract

Applications of neural networks to pattern classification problems in underwater acoustics have been an active area of research. Often, due to lack of a sufficient amount of data, the training data may not accurately represent the probability dlstributions of the classes to be classified. This paper gives a simple and illustrative simulation example of a neural network performing unsatisfactorily under such circumstances. During training, a back propagation neural network classifier learns to recognize two classes of waveforms. Waveforms in Class 1 have two major peaks and low SNR. Waveforms in Class 2 have one major peak and high SNR. In testing it was found that the neural network classifier tuned in to their difference in SNR rather than the number of peaks.