Combining Neural Networks and Histogram Layers for Underwater Target Classification

Abstract

To detect targets, sea vessels largely rely on passive sonar, which records sounds with an underwater microphone. However, techniques for processing and analyzing passive sonar data often struggle to disentangle the complex patterns in target recordings.

To better capture statistical features within passive sonar data, a team from Lincoln Laboratory and the Advanced Vision and Learning Lab at Texas A&M University are adding local histogram layers into neural network architectures.

This project employs two types of neural networks for automated feature learning that together can capture local relationships within audio signals while incorporating signal time dependencies.