23-05-2012, 04:18 PM
Design and Evaluation of adaptive Filter Using
Normalized LMS Algorithm
Design and Evaluation of adaptive Filter Using Normalized LMS Algorithm.pdf (Size: 359.22 KB / Downloads: 36)
Abstract
Noise problems in the environment have gained attention due to the tremendous growth of technology that has led to noisy engines, heavy machinery, high speed wind buffeting and other noise sources. The problem of controlling the noise level has become the focus of a tremendous amount of research over the years. In last few years various adaptive algorithms are developed for noise cancellation.
Introduction
In the process of transmission of information from the source to receiver side in all the channels, noise from the surroundings automatically gets added to the signal. This acoustic noise [1] picked up by microphone is undesirable, as it reduces the perceived quality or intelligibility of the audio signal. The problem of effective removal or reduction of noise is an active area of research [2]. The usage of adaptive filters is one of the most popular proposed solutions to reduce the signal corruption caused by predictable and unpredictable noise added to the source signal. An adaptive filter [3] has the property of self-modifying its frequency response to change the behavior in time domain, allowing the filter to adapt the response to the input signal characteristics change. Because of this capability, overall performance and the construction flexibility,
Design of adaptive filter
Steepest Descent Algorithm
An adaptive filter is required to find a solution for its tap-weight vector that satisfies the normal equation. A procedure is to use the method of steepest descent, which is one of the oldest methods of optimization.
Least-Mean-Squares (LMS) Algorithm
The LMS algorithm remedies this problem by adapting the filter weighs according to the incoming audio data as it is being received. The LMS algorithm is robust enough for a variety of signal conditions due to its adaptive nature. The LMS algorithm involves the computation of the output of a linear filter in response to the noise reference and the generation of the estimation error between this output and the desired respons[6]e. The estimation error is used in the adjustment of the filter weighs. If it were possible to make exact measurements of the gradient vector at each iteration, and if the step-size parameter is suitably chosen, then the tap-weight vector computed by using the method of steepest-descent would indeed converge to the optimum solution . A significant feature of LMS is its simplicity;
Result and discussion
The graphs above shows the result obtained when we design the adaptive filtering. Surface contour, frequency response, learning curves and misalignment curves all are obtained and shown with best result. The primary measure of performance in the LMS adaptive filter is the mean square error.
Conclusion
The implementation of algorithms was successfully achieved, with results that have a really good response as shown in the previous figures. The simulation results show that LMS algorithm give good results in noise cancelling. To complete the task of noise reduction LMS filtering results is relatively good, the requirements length of filter is relatively short, it has a simple structure and small operation and is easy to realize. The signal to noise ratio was measured as given in table 1. However, when the adaptive filter operates in a non-stationary environment, the bottom of the error performance surface continually moves, while the orientation and curvature of the surface may be changing too.