# Histogram Layer Time Delay Neural Networks for Passive Sonar Classification Source: [Histogram Layer Time Delay Neural Networks for Passive Sonar Classification]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:35:15.802Z Authors: Jarin Ritu, Ethan Barnes, Riley Martell, Alexandra Van Dine, Joshua Peeples Published: 2023-07-25T19:47:26.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Classification on DeepShip](): Classification; DeepShip; Accuracy: 59.62. - [Classification on VTUAD](): Classification; VTUAD; Accuracy: 80.49. - [Acoustic Scene Classification on TUT Acoustic Scenes](): Acoustic Scene Classification; TUT Acoustic Scenes; Accuracy: 58.13. - [Audio Classification on ESC-50](): Audio Classification; ESC-50; Accuracy: 72.33. ## Original sources and implementation [Original paper]() [PDF]()