# Histogram Layer Time Delay Neural Networks for Passive Sonar Classification

arXiv: https://arxiv.org/abs/2307.13788 · HTML: https://mlsanity.com/paper/2307.13788/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [Learning Statistical Texture for Semantic Segmentation](https://mlsanity.com/paper/2103.04133/) (2021)
- [Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation](https://mlsanity.com/paper/2305.03944/) (2023)
- [Histogram Layers for Texture Analysis](https://mlsanity.com/paper/2001.00215/) (2020)
- [Integrating Frequency Translational Invariance in TDNNs and Frequency Positional Information in 2D ResNets to Enhance Speaker Verification](https://mlsanity.com/paper/2104.02370/) (2021)

## Built on by (most cited first, 5 of 5)

- [Neural Edge Histogram Descriptors for Underwater Acoustic Target Recognition](https://mlsanity.com/paper/2503.13763/) (2025)
- [Histogram-based Parameter-efficient Tuning for Passive and Active Sonar Classification](https://mlsanity.com/paper/2504.15214/) (2025)
- [Cross-Domain Knowledge Transfer for Underwater Acoustic Classification Using Pre-trained Models](https://mlsanity.com/paper/2409.13878/) (2024)
- [Investigation of Time-Frequency Feature Combinations with Histogram Layer Time Delay Neural Networks](https://mlsanity.com/paper/2409.13881/) (2024)
- [Structural and Statistical Audio Texture Knowledge Distillation for Acoustic Classification](https://mlsanity.com/paper/2501.01921/) (2025)

## Abstract (arXiv)

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.