A Feed-forward Switched Adaptive Filtering configuration for Underwater Acoustic Signal Denoising Technique with low-complexity

Deekshitha Adusumalli, Swapnil Maiti, S. Hannah Pauline, Gerard Dooly, Samiappan Dhanalakshmi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Underwater acoustic communication is difficult by virtue of the noise that exists undersea. This can be caused by both natural phenomena, like waves and currents, as well as artificial sources such as ships and boats. In order to reduce or eliminate this interference, it is necessary to process the signal before further processing. One way of doing this is through denoising. However, because noise constantly fluctuates in intensity, predicting its future behavior becomes almost impossible. To address this problem, we propose a new method for filtering underwater communications using an superlative feedfoward switched adaptive filter model using Signed Error variant of Least Mean Square (SELMS) and Signed Data variant of Least Mean Square (SDLMS) algorithm that takes into account signed form LMS values. The effectiveness of the filter is tested using a clean fish sound that has been tainted by noises from underwater vessels from the ShipsEar database.

Original languageEnglish
Title of host publicationOCEANS 2023 - Limerick, OCEANS Limerick 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350332261
DOIs
Publication statusPublished - 2023
Event2023 OCEANS Limerick, OCEANS Limerick 2023 - Limerick, Ireland
Duration: 5 Jun 20238 Jun 2023

Publication series

NameOCEANS 2023 - Limerick, OCEANS Limerick 2023

Conference

Conference2023 OCEANS Limerick, OCEANS Limerick 2023
Country/TerritoryIreland
CityLimerick
Period5/06/238/06/23

Keywords

  • adaptive noise cancellation
  • Least Mean Square
  • SDLMS
  • SELMS

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