Histogram Layer Time Delay Neural Networks for Passive Sonar Classification

TL;DR

This paper introduces a novel neural network architecture combining time delay neural networks and histogram layers to improve underwater acoustic target classification by effectively capturing statistical contexts, outperforming baseline models.

Contribution

The paper proposes a new method that integrates histogram layers with time delay neural networks for enhanced feature learning in passive sonar classification.

Findings

Outperforms baseline models in target recognition accuracy

Effectively captures statistical contexts in acoustic data

Demonstrates utility in underwater passive sonar applications

Abstract

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.