Histogram Layer Time Delay Neural Networks for Passive Sonar
  Classification
Jarin Ritu Ethan Barnes Riley Martell Alexandra Van Dine Joshua Peeples
Underwater acoustic target detection in remote marine sensing operations is
challenging due to complex sound wave propagation. Despite the availability of
reliable sonar systems, target recognition remains a difficult problem. Various
methods address improved target recognition. However, most struggle to
disentangle the high-dimensional, non-linear patterns in the observed target
recordings. In this work, a novel method combines a time delay neural network
and histogram layer to incorporate statistical contexts for improved feature
learning and underwater acoustic target classification. The proposed method
outperforms the baseline model, demonstrating the utility in incorporating
statistical contexts for passive sonar target recognition. The code for this
work is publicly available.