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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Source localization using deep neural networks in a shallow water environment.

Zhaoqiong Huang¹, Ji Xu¹, Zaixiao Gong²

¹Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, No. 21 North 4th Ring Road, Haidian District, Beijing 100190, People's Republic of China.

The Journal of the Acoustical Society of America

|June 3, 2018

Summary

This summary is machine-generated.

Deep neural networks (DNNs) effectively locate broadband sources in shallow waters. These deep learning models improve underwater acoustic source localization, even with limited environmental data.

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Area of Science:

Acoustics
Signal Processing
Machine Learning

Background:

Accurate source localization in shallow water is challenging due to complex sound propagation.
Deep neural networks (DNNs) offer powerful tools for modeling nonlinear relationships in acoustic data.

Purpose of the Study:

To apply DNNs for estimating the range and depth of a broadband source in shallow water environments.
To evaluate two distinct DNN architectures for underwater source localization.

Main Methods:

A two-stage method using extracted eigenvectors as input to a time delay neural network.
A convolutional neural network-feed-forward neural network (CNN-FNN) architecture directly processing raw multi-channel waveforms.

Main Results:

Both proposed DNN methods demonstrated effectiveness in source localization.
DNNs performed well even in complex environments with sparse environmental information.

Conclusions:

Deep neural networks are a viable and effective tool for underwater acoustic source localization.
The proposed DNN approaches offer robust performance across varied shallow water conditions.