Sound Source Localization in a Multipath Environment Using Convolutional Neural Networks

TL;DR

This paper introduces CNN-based methods for localizing sound sources in shallow water environments with multipath effects, improving accuracy over traditional techniques by leveraging cepstrogram and cross-correlogram inputs.

Contribution

It demonstrates the effectiveness of convolutional neural networks in enhancing passive sound source localization in complex multipath shallow water environments.

Findings

CNNs improve localization accuracy in multipath conditions

Use of cepstrogram and cross-correlogram inputs enhances performance

Real-world data confirms the method's effectiveness

Abstract

The propagation of sound in a shallow water environment is characterized by boundary reflections from the sea surface and sea floor. These reflections result in multiple (indirect) sound propagation paths, which can degrade the performance of passive sound source localization methods. This paper proposes the use of convolutional neural networks (CNNs) for the localization of sources of broadband acoustic radiated noise (such as motor vessels) in shallow water multipath environments. It is shown that CNNs operating on cepstrogram and generalized cross-correlogram inputs are able to more reliably estimate the instantaneous range and bearing of transiting motor vessels when the source localization performance of conventional passive ranging methods is degraded. The ensuing improvement in source localization performance is demonstrated using real data collected during an at-sea experiment.