Journal of Engineering Science and Technology Review 10 (2) (2017) 150- 160 Review Article A Survey with Emphasis on Adaptive filter, Structure, LMS and NLMS Adaptive Algorithm for Adaptive Noise Cancellation System Rachana Nagal 1, *, Pradeep Kumar 2 and Poonam Bansal 3 Department of ECE/ICE, Amity School of Engg and Technology, New Delhi-110061 Received 30 September 2015; Accepted 12 March 2017 ___________________________________________________________________________________________ Abstract This paper discusses the evolution of adaptive filtering, filter structure, adaptive algorithms used for noise cancellation over the past five decades. The field of adaptive signal processing has been matter of research for over 50-60 years. The major growth occurred in this field in eighties because of the availability of implementation tools and textbooks. Adaptive signal processing has made a significant contribution in the last 50 years. The applications of adaptive signal processing are very appealing because of its properties like low costing, constancy, fidelity, small sizes, and adjustability. This revolutionary change brought along with the problems of noise and the solution is the design of the adaptive filter. This paper mainly focused on adaptive filter, and its structure, the Least Mean Square Algorithm (LMS) and Normalized Least Mean Square Algorithm (NLMS), used for noise cancellation. This paper could serve as a survey for beginners and as a reference to select the related reference of their field. Keywords: Adaptive filter, Adaptive filter Structure, Adaptive Algorithm, Least Mean Square algorithm, Noise Cancellation. __________________________________________________________________________________________ 1. Introduction The designing of electronic system must depend upon the different type of noise and distortion. The noise can be added during the process of signal through a channel due to the slow or fast variations of its properties. As most of the time the variations are unknown, so it is adaptive filtering that completely eliminates the signals distortion. So the adaptive system is something whose structure is adjustable as per its performance or behaviour. The main quality of the adaptive model is its time variance and self-adjusting nature. The adaptive filter with adaptive algorithm finds its application in adaptive noise cancellation. The concept behind adaptive noise cancellation is discussed in the Fig 1 below. An input signal x n passes to a sensor that also accepts noiseN ! (n). This noise N0 (n) is not correlated with the signal. The input signal is mixed with the noise and forms a noise corrupted signal {Sig n = x n + N ! (n)} or the noisy signal. There is a second sensor which captures noise N ! (n) which is not related with the signal but somehow correlated with the first noise signal N ! (n). This gives the reference input to the adaptive noise canceller model. The output y(n) is produced by filtering the noise N ! (n) that should be as close as possible to N ! (n). The generated outputs get subtracted from the noise corrupted signal and produce the required signal d(n) = {Sig(n) – y(n)}. The adaptive filter is an adequate concept that can be able to adjust its transfer function via an adaptive algorithm. The adaptive algorithm is to minimize the error signal. This error signal is feedback to adaptive filter. The adaptive algorithm now try to minimize this error signal as minimum as possible. This error signal is an important parameter to judge the accuracy of algorithm along with its convergence. So to design an adaptive noise cancellation system the points listed below needs to be taken care of: Fig. 1. Basic Concept of Noise Cancellation 1. The input signals which is being treated by the adaptive filter. 2. The structure of the filter that shows the mathematical relation between output and input signal. 3. Parameters inside the structure that can change the input output relationship of the filter. 4. The most important is adaptive algorithm, required to update the parameters of the filter. JOURNAL OF Engineering Science and Technology Review www.jestr.org Jestr ______________ *E-mail address: rachana.nagal@gmail.com ISSN: 1791-2377 © 2017 Eastern Macedonia and Thrace Institute of Technology. All rights reserved.