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.

水下声学目标检测在远程海洋感知操作中具有挑战性，本文提出一种融合时间延迟神经网络和直方图层的新方法，以改善特征学习和水下声学目标分类的统计上下文，并证明了其在被动声纳目标识别中的实用性。

用于被动声纳分类的直方图层时延神经网络

Histogram Layer Time Delay Neural Networks for Passive Sonar  Classification

Underwater acoustic target recognition is a challenging task owing to the
intricate underwater environments and limited data availability. Insufficient
data can hinder the ability of recognition systems to support complex modeling,
thus impeding their advancement. To improve the generalization capacity of
recognition models, techniques such as data augmentation have been employed to
simulate underwater signals and diversify data distribution. However, the
complexity of underwater environments can cause the simulated signals to
deviate from real scenarios, resulting in biased models that are misguided by
non-true data. In this study, we propose two strategies to enhance the
generalization ability of models in the case of limited data while avoiding the
risk of performance degradation. First, as an alternative to traditional data
augmentation, we utilize smoothness-inducing regularization, which only
incorporates simulated signals in the regularization term. Additionally, we
propose a specialized spectrogram-based data augmentation strategy, namely
local masking and replicating (LMR), to capture inter-class relationships. Our
experiments and visualization analysis demonstrate the superiority of our
proposed strategies.

本研究提出在数据量有限的情况下，采用平滑性正则化，即只在正则项中加入模拟信号，并使用专门的光谱图基础数据增强策略（LMR），以避免性能下降的风险来增强模型的泛化能力。实验证明了该方法具有优越性。