TY - GEN
T1 - A Feed-forward Switched Adaptive Filtering configuration for Underwater Acoustic Signal Denoising Technique with low-complexity
AU - Adusumalli, Deekshitha
AU - Maiti, Swapnil
AU - Pauline, S. Hannah
AU - Dooly, Gerard
AU - Dhanalakshmi, Samiappan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - adaptive noise cancellation
KW - Least Mean Square
KW - SDLMS
KW - SELMS
UR - http://www.scopus.com/inward/record.url?scp=85173660784&partnerID=8YFLogxK
U2 - 10.1109/OCEANSLimerick52467.2023.10244466
DO - 10.1109/OCEANSLimerick52467.2023.10244466
M3 - Conference contribution
AN - SCOPUS:85173660784
T3 - OCEANS 2023 - Limerick, OCEANS Limerick 2023
BT - OCEANS 2023 - Limerick, OCEANS Limerick 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 OCEANS Limerick, OCEANS Limerick 2023
Y2 - 5 June 2023 through 8 June 2023
ER -