Abstract: Heart sound signals will replicate the physiological and pathological characteristics of the heart. every heart beat is incredibly advanced and short and also the main frequency of heart sound signals is within the range of 10Hz to 250Hz. Phonocardiogram will record heart sounds, noise and also the extra sounds. Therefore it's a crucial complement to form up center diagnostic technique examination. Heart sounds are very weak acoustic signals. Within the method to gather heart sound signals it's prone to external acoustic signals and electrical noise interference, especially, the friction caused by subjects respiratory or body movement. The sounds made by friction within the phonocardiogram may produce to an enormous busy signal. Thus, it is vital to research heart sound accurately and eliminates the busy signal with success throughout pre-processing. The objective of this work 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 topics over the years. This work 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. This Paper involves the study of the principles of adaptive Noise Cancellation (ANC) and its Applications. Adaptive noise Cancellation is another technique of estimating signals corrupted by additive noise or interference. Its advantage lies in this, with no apriority estimates of signal or noise, levels of noise rejection are attainable that would be difficult or not possible to achieve by other signal processing methods of removing noise. Its cost, inevitably, is that it wants two inputs - a primary input containing the corrupted signal and a reference input containing noise correlate in some unknown approach with the first noise. The reference input is adaptively filtered and subtracted from the first input to get the signal estimate. Adaptive filtering before subtraction permits the treatment of inputs that are settled or random, stationary or time-variable. The result of uncorrelated noises in primary and reference inputs, and presence of signal parts within the reference input on the ANC performance is investigated. it's shown that within the absence of uncorrelated noises and once the reference is free from signal, noise within the primary input can be eliminated while not signal distortion. A configuration of the adaptive noise canceller that does not need a reference input and is incredibly helpful applications is additionally conferred.
Keywords: LMS (Least Mean Square), NLMS (Normalized Least Mean Square), RLS (Recursive Least Square), MSE (Mean Squared Error).