Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/5562
Title: AI Based Variable Step Size Block Least Mean Square Filter for Noise Cancellation System
Authors: R, Manikandan
Kumar, Sathesh.K.
S., Ramkumar
Keywords: Adaptive Filter
Blocked Least Mean Square (Blms)
Fir Filter
Normalized Lms
Issue Date: 2023
Publisher: International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 Proceedings
Citation: pp. 122125
Abstract: Most of the Active Noise Cancellation (ANC) systems working properly in lowfrequency noises only. To make it suitable for isolating highfrequency noise, it needs an additional circuit which consumes more energy. This problem is mitigated in this study by designing a Variable Step size Block Least Mean Square (VSBLMS) filter which is suitable for an effective noise cancellation system. VSBLMS filter is designed with RCA to make a design area efficient and it is designed with a novel adder to achieve high speed as well as less energy consumption. The proposed filter is designed and simulated using Xilinx ISE 13.2. The simulation results shows that the proposed VSBLMS filter design mitigates the unwanted noises in various frequency bands. The proposed VSBLMS reduces the energy consumption by 9.32%, 27.63%, 13.53%, 11.80%, 10.71 %, 13.14% and 9.26% when compared with state of the art methods. © 2023 IEEE.
URI: https://doi.org/10.1109/ICSSAS57918.2023.10331847
http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/5562
ISBN: 9798350300857
Appears in Collections:Conference Papers

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