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Implementation of Adaptive Noise Canceller in Digital Filter for Various Applications

Swati S. Godbole, Dr. Sanjay B. Pokle


This Paper involves the study of the principles of Adaptive Noise Cancellation (ANC) and its Applications. Adaptive Noise Cancellation is an alternative technique of estimating signals corrupted by additive noise or interference. It needs two inputs - a primary input containing the corrupted signal and a reference input containing noise correlated in some unknown way with the primary noise. The reference input is adaptively filtered and subtracted from the primary input to obtain the signal estimate. Adaptive filtering before subtraction allows the treatment of inputs that are deterministic, stationary or time-variable. Noise cancellation is a common occurrence in todays telecommunication systems. Noise is unwanted signal that is disturbing especially in communication system. The main purpose of this paper is to eliminate noise that exists in input signal, which makes it difficult to understand. The signal interference caused by noise is distracting to both users and causes a reduction in the quality of the communication. This paper focuses on the use of adaptive filtering techniques to reduce this unwanted noise, thus increasing communication quality. Adaptive filters are a class of filters that iteratively alter their parameters in order to minimize a function of the difference between a desired target output and their output. In the case of adaptive noise cancellation in telecommunications, the optimal output is a noisy signal that accurately emulates the unwanted noise signal. This is then used to negate the noise in the return signal. The better the adaptive filter emulates this noise, the more successful the cancellation will be. Various applications of the ANC can be studied. Computer simulations for some cases are carried out using Matlab software and experimental results are presented that illustrate the usefulness of Adaptive Noise Canceling Technique. This paper examines various techniques and algorithms of adaptive filtering, employing discrete signal processing in MATLAB. Simulation was utilized by using MATLAB software to eliminate the noise. The strategies & design methodologies of of Adaptive Noise Canceller using the least mean square (LMS) algorithm is considered in this paper.


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S. S. Godbole, “Adaptive Noise Canceller in Digital Filter for Various Applications: A Design Approach”, 5-7 January 2011, VelTech Chennai.

S. A. Hadei, “A Family of Adaptive Filter Algorithms in Noise Cancellation for Speech Enhancement”, International Journal of Computer and Electrical Engineering, Vol. 2, No. 2, April 2010, pp 307-315

Kunal Kumar Das, Jutendriya Kumar Satapathy, “Frequency-Domain Block Filtered-x NLMS Algorithm for Multichannel ANC”, ICETET 2008, Nagpur, pp 1293-1297

Boo-Shik Ryu, Jae-Kyun Lee, “The Performance of an adaptive noise canceller with DSP processor”, 40th Southeastern Symposium on System Theroy, New Orleans, USA, March 16-18, 2008, pp 42-45.

D. Nicolae, R. Romulus, “Noise canceling in audio signal with adaptive filter”, University of Oradea, Vol. 45, Number 6, 2004, pp 599-602

B. Widrow, J. R. Glover, J. M. McCool, J. Kaunitz, C. S. Williams, R. H. Hearn, J. R. Zeidler, E. Dong and R. C. Goodlin, “Adaptive noise canceling: Principles and applications”, Proc. IEEE, vol. 63, Dec. 1975, pp. 1692-1716.

G. Long, F. Ling and J. G. Proakis, “The LMS algorithm with delayed coefficient adaptation,” IEEE Trans. on ASSP, vol. 37, Sept. 1989, pp. 1397-1405.

C.-L.Wang, “Bit-serial VLSI implementation of delayed LMS adaptive FIR filters,” IEEE Trans. Signal Process., vol. 42, Aug. 1994, pp. 2169–2175.

L. K. Ting, R. F. Woods and C. F. N. Cowan, “Virtex FPGA Implementation of a Pipelined Adaptive LMS Predictor for Electronic Support Measures Receivers,” IEEE Trans. VLSI Syst., vol. 13, Jan. 2005, pp. 86-95.

M. D. Meyer and D. P. Agrawal, “A high sampling rate delayed LMS filter architecture,” IEEE Trans., Circuits Syst. II, Analog Digit. Signal Process. vol. 40, Nov. 1993, pp. 727–729.

L.-K. Ting, “Algorithms and FPGA implementations of adaptive LMS-based predictors for radar pulse identification,” Ph.D. dissertation, Queen’s Univ. Belfast, N. Ireland, Jul. 2001.

B. Widrow, J. R. Glover, J. M. McCool and et al., ”Adaptive Noise Canceling: Principles and Applications”, Proc. IEEE, vol. 63, Dec. 1975, pp. 1692-1716.

P. Waldeck and N. Bergmann, “Evaluating software and hardware implementations of signal-processing tasks in an FPGA,” in Proc. IEEE International Conference on Field-Programmable Technology, Brisbane, Australia, Dec. 2004, pp. 299-302.

A. Elhossini, S. Areibi and R. Dony, “An FPGA Implementation of the LMS Adaptive Filter for Audio Processing,” in Proc. IEEE International Conference on Reconfigurable Computing and FPGAs, Sept. 2006, pp. 1-8.

D. P. Das, G. Panda and S. M. Kuo, “New block filtered –x LMS algorithm for active noise control systems”, IET Signal Processing, 2007, 1(2), pp 73-81

S. Haykin, Adaptive Filter Theory, Prentice-Hall, third edition, 2002.

B. Widrow, et al., “Adaptive Noise Cancelling: Principles and Applications”, Proc. IEEE, vol. 63, pp.1692-1716, Dec. 1975.

Simon Haykin, Adaptive Filter Theory, Prentice Hall, II. Edition

John R. Glover, Jr., “Adaptive Noise Canceling Applied to Sinusoidal Interferences”, IEEE Trans. ASSP, Vol. ASSP-25, No. 6, pp. 484-491, Dec. 1977.

J.R. Zeidler et al., “Adaptive Enhancement of Multiple Sinusoids in Uncorrelated Noise”, IEEE Trans. ASSP, Vol. ASSP-26, No. 3, pp. 240-254, June 1978.

D. W. Tufts, “Adaptive Line Enhancement and Spectrum Analysis”, Proc. IEEE (Letts.), vol. 65, pp.169-170, Jan. 1977


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