Noise Cancellation Using Adaptive Filter for PCG Signal

Naveen Dewangan, and R. M. Potdar

Abstract: The objective of this paper is to serve as how Noise can be combated using adaptive filter for PCG signal. The problem of controlling the noise level has been one of the research topic over the years. This paper focuses on Adaptive filtering algorithms and some of the applications of adaptive filter. The main concept is to use the LMS (Least-Mean- Square) algorithm to develop an adaptive filter that can be used in Adaptive noise Cancellation (ANC) application. In this paper we will learn the various algorithms of LMS (Least Mean Square), NLMS (Normalized Least Mean Square) and RLS (Recursive Least Square) on MATLAB platform with the intention to compare their performance in noise cancellation. The adaptive filter in MATLAB with a noisy tone signal and white noise signal and analyze the performance of algorithms in terms of MSE (Mean Squared Error), percentage noise removal, Signal to Noise Ratio, computational complexity and stability. The Adaptive Filter maximizes the signal to noise ratio & minimize the Mean Squared Error and compare their performance with respect to stability. Adaptive Noise Canceller is useful to improve the S/N ratio. The Adaptive Filter minimizes the mean squared error between a primary input, which is the noisy PCG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with PCG in the primary input. Keywords: LMS (Least Mean Square), NLMS (Normalized Least Mean Square), RLS (Recursive Least Square), MSE (Mean Squared Error).

Title:	Noise Cancellation Using Adaptive Filter for PCG Signal
Author Name:	Naveen Dewangan, and R. M. Potdar
Abstract:	Abstract: The objective of this paper is to serve as how Noise can be combated using adaptive filter for PCG signal. The problem of controlling the noise level has been one of the research topic over the years. This paper focuses on Adaptive filtering algorithms and some of the applications of adaptive filter. The main concept is to use the LMS (Least-Mean- Square) algorithm to develop an adaptive filter that can be used in Adaptive noise Cancellation (ANC) application. In this paper we will learn the various algorithms of LMS (Least Mean Square), NLMS (Normalized Least Mean Square) and RLS (Recursive Least Square) on MATLAB platform with the intention to compare their performance in noise cancellation. The adaptive filter in MATLAB with a noisy tone signal and white noise signal and analyze the performance of algorithms in terms of MSE (Mean Squared Error), percentage noise removal, Signal to Noise Ratio, computational complexity and stability. The Adaptive Filter maximizes the signal to noise ratio & minimize the Mean Squared Error and compare their performance with respect to stability. Adaptive Noise Canceller is useful to improve the S/N ratio. The Adaptive Filter minimizes the mean squared error between a primary input, which is the noisy PCG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with PCG in the primary input. Keywords: LMS (Least Mean Square), NLMS (Normalized Least Mean Square), RLS (Recursive Least Square), MSE (Mean Squared Error).
Cite this article:	Naveen Dewangan, and R. M. Potdar , " Noise Cancellation Using Adaptive Filter for PCG Signal " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 3, Issue 4, July - August 2014 , pp. 038-043 , ISSN 2278-6856.
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