ADAPTIVE NOISE CANCELLATION

In numerous applications of signal processing, communications and biomedical we are faced with the necessity to remove noise and distortion from the signals. Adaptive filtering is one of the most important areas in digital signal processing to remove background noise and distortion. As received signal is continuously corrupted by noise where both received signal and noise signal both changes continuously, then this arise the need of adaptive filtering. In last few years various adaptive algorithms are developed for noise cancellation. The least mean square (LMS) algorithm is an important variant of the classical adaptive linear filtering algorithm. It possesses many advantages over other algorithm, including having a good convergence and providing for an automatic time- varying choice of the LMS stepsize parameter that affects the stability, steady-state mean square error (MSE), and convergence speed of the algorithm. An auxiliary fixed step-size that is often introduced in the LMS algorithm has the advantage that its stability region (step-size range for algorithm stability) is independent of the signal statistics. This paper describes the development of an adaptive noise cancellation algorithm like LMS(Least Mean Square)for effective recognition of signal on MATLAB platform .We simulat e the adaptive filter in MATLAB with noisy signal and obtained result shows that LMS algorithm eliminates noise from noisy signal and get desired result at the output.