A Novel Method for Classification and Modelling of Underwater Acoustic Communication through Machine Learning and Image Processing - Technique

Aug 3, 2026

Growing underwater operations increase the demand for dependable underwater acoustic communication. The harsh underwater environment limits conventional voice communication, so accurate channel simulation becomes essential. This study uses real measurements from a water reservoir and a lake to assess machine learning-based reconstruction of the underwater acoustic channel, focusing on LSTM and DNN models. Results confirm LSTM’s effectiveness for channel simulation with low mean absolute percentage error, and the work further applies image processing to recognize objects in the acoustic environment.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Underwater environment challenges

Acoustic vs electromagnetic communication

Motivation for machine learning channel reconstruction

Journal of Advanced Zoology ISSN: 0253-7214 Volume 44 Special Issue-02 Year 2023 Page 1026:1038 A Novel Method for Classification and Modelling of Underwater Acoustic Communication through Machine Learning and Image Processing Technique 1K SAIKUMAR, School of engineering, department ofCSE, Malla Reddy University, Maisammaguda, Dulapally, Hyderabad, Telangana 500043, [saikumarkayam4@ieee.org](saikumarkayam4@ieee.org) 1*MAKKAPATI HIMAJA, Assistant Professor, Department of ECE, R.V.R.&J.C. COLLEGE of ENGINEERING A.P INDIA, [Email: ](Email: makkapatihimaja@rvrjc.ac.in)[makkapatihimaja@rvrjc.ac.in](Email: makkapatihimaja@rvrjc.ac.in) 2D. V. DIVAKARA RAO, Associate Professor, Department ofC. S. E, Raghu Engineering College, Dakamarri, Bheemunipatnam Mandal, Visakhapatnam, [divakararao.dusi@raghuenggcollege.in](divakararao.dusi@raghuenggcollege.in)[ ](divakararao.dusi@raghuenggcollege.in)3DR.P.CHANDRA KANTH, Associate Professor in the department ofCSE at Audisankara College of Engineering & Technology ASCET (Autonomous), Gudur, Tirupathi(DT). India, 524101, [chandrakanthc4u@gmail.com](chandrakanthc4u@gmail.com)[ ](chandrakanthc4u@gmail.com)4NP LAVANYA KUMARI Assistant Professor (c) Department of computer science and systems engineering Andhra [University ](University lavanyanr@yahoo.co.in)[lavanyanr@yahoo.co.in](University lavanyanr@yahoo.co.in) 5R REVATHI, Associate professor, Department of ECE, Koneru Lakshmaiah Education Foundation, India 522502 Mail [id : ](id : rrevathi@kluniversity.in)[rrevathi@kluniversity.in](id : rrevathi@kluniversity.in) | Article History
Received: 27Aug 2023
Revised: 28Sept 2023
Accepted: 06Oct 2023
CC License
CC-BY-NC-SA 4.0 | ABSTRACT
The increasing prevalence of underwater activities has highlighted the urgent need for reliable underwater acoustic communication systems. However, the challenging nature of the underwater environment poses significant obstacles to the implementation of conventional voice communication methods. To better understand and improve upon these systems, simulations of the underwater audio channel have been developed using mathematical models and assumptions. In this study, we utilize real-world information gathered from both a measured water reservoir and Lake to evaluate the ability of machine learning and machine learning methods, specifically Long Short-Term Memory (LSTM) and Deep Neural Network (DNN), to accurately reconstruct the underwater audio channel. The outcomes validate the efficiency of machine learning methods, particularly LSTM, in accurately simulating the underwater acoustic communication channel with low mean absolute percentage error. Additionally, this research also includes an image processing to identify the objects present the in the acoustic environment. Keywords: Underwater acoustic communication, Machine learning, deep neural network, image processing | | --- | --- | 1. Introduction The interest in research on underwater wireless communication has been growing among both civilian and military organizations. This is due to the increasing use of submerged actions, that are military surveillance, submerged mining, fiber optic and pipeline installation, and aquatic/biological research. Researchers in fields like marine biology, engineering, and 1026 Available online at: [https://jazindia.com](https://jazindia.com) A Novel Method for Classification and Modelling of Underwater Acoustic Communication through Machine Learning and Image Processing Technique other disciplines require tools to better understand the underwater environment, as it covers over 71% of the earth's surface (Halakarnimath and Sutagundar 2021) . A robust underwater acoustic communication infrastructure is necessary for the growth of undersea operations. The underwater environment is one of the most challenging for communication, due to factors such as slow propagation, limited bandwidth, and large multipath delay spread. Two wireless comm

FAQs

Why are underwater acoustic communication systems difficult to implement with conventional voice methods?

The underwater environment introduces slow propagation, limited bandwidth, and large multipath delay spread, which hinder reliable communication.

Which machine learning models are evaluated for reconstructing the underwater audio channel?

The study evaluates Long Short-Term Memory (LSTM) and Deep Neural Network (DNN) approaches.

What additional capability does the research include beyond channel reconstruction?

It applies image processing to identify objects present in the acoustic environment.

Aug 3, 2026

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