28-11-2012, 03:47 PM
Noise cancellation using adaptive algorithms
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I. INTRODUCTION
Acoustic noise problems becomes more pronounce as increase in number of industrial equipment such as engines, transformers, compressors and blowers are in use. The traditional approach to acoustic noise cancellation uses passive techniques such as enclosures, barriers and silencers to remove the unwanted noise signal [1][2]. Silencers are important for noise cancellation over broad frequency range but ineffective and costly at low frequencies. Mechanical vibration is a type of noise that creates problems in all areas of communication and electronic appliances. Signals are carriers of information, both useful and unwanted. Extracting or enhancing the useful information from a mix of conflicting information is a simplest form of signal processing. Signal processing is an operation designed for extracting, enhancing, storing, and transmitting useful information. Hence signal processing tends to be application dependent. In contrast to the conventional filter design techniques, adaptive filters do not have constant filter coefficients and no priori information is known. Such a filter with adjustable parameters is called an adaptive filter. Adaptive filter adjust their coefficients to minimize an error signal and can be realized as finite impulse response (FIR), infinite impulse response (IIR), lattice and transform domain filter [4]. The most common form of adaptive filter is the transversal filter using least mean square (LMS) algorithm and NLMS algorithm.
LEAST MEAN SQUARE ALGORITHM
To make exact measurements of the gradient vector at each iteration n, and if the step-size parameter is suitably chosen then the tap-weight vector computed by using the steepest descent algorithm would converge to the optimum wiener solution. The exact measurements of the gradient vector are not possible and since that would require prior knowledge of both the autocorrelation matrix R of the tap inputs and the cross correlation vector p between the tap inputs and the desired response, the optimum wiener solution could not be reached [3]. Consequently, the gradient vector must be estimated from the available data when we operate in an unknown environment.
EXPERIMENTAL RESULT
In this section we compare the performance of the LMS and NLMS algorithms as noise canceller. The algorithms are implemented according to the steps. Figure 3 shows that, the input sinusoidal signal and random noise signal. Figure 4 shows that, the noise present in the sinusoidal signal and is eliminated using LMS algorithm of order 5. Figure 5 shows that, the noise present in the sinusoidal signal and is eliminated using NLMS algorithm of order 5.
CONCLUSION
This paper has described an application in which the use of an LMS and NLMS adaptive filter is particularly appropriate. The main goal of this paper is to investigate the application of an algorithm based on adaptive filtering in noise cancellation problem. The LMS algorithm has been shown to produce good results in a noise cancellation problem.