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Implementation of Adaptive Noise Canceller Using Instantaneous Gain Control Algorithm  

Lee, Jae-Kyun (Department of Computer and Communication Engineering Daegu University)
Kim, Chun-Sik (Department of Computer and Communication Engineering Daegu University)
Lee, Chae-Wook (Department of Computer and Communication Engineering Daegu University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.46, no.6, 2009 , pp. 95-101 More about this Journal
Abstract
Among the adaptive noise cancellers (ANC), the least mean square (LMS) algorithm has probably become the most popular algorithm because of its robustness, good tracking properties, and simplicity of implementation. However, it has non-uniform convergence and a trade-off between the rate of convergence and excess mean square error (EMSE). To overcome these shortcomings, a number of variable step size least mean square (VSSLMS) algorithms have been researched for years. These LMS algorithms use a complex variable step method approach for rapid convergence but need high computational complexity. A variable step approach can impair the simplicity and robustness of the LMS algorithm. The proposed instantaneous gain control (IGC) algorithm uses the instantaneous gain value of the original signal and the noise signal. As a result, the IGC algorithm can reduce computational complexity and maintain better performance.
Keywords
LMS; VSSLMS; ANC; ICG;
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1 Boll, S. F. and D. C. Pulsipher, Suppression of acoustic noise in speech using two microphone adaptive noise cancellation, IEEE Trans. Acoust., Speech, Signal Processing, vol.ASSP-28, no.6, 1980
2 Widrow, B., et al., Adaptive noise canceling: principles and applications, Proc. IEEE, vol.63, pp.1692-1762, 1975   DOI   ScienceOn
3 Joonwan Kim and Poularikas, A. D. , Comparison of two proposed methods in adaptive noise canceling, IEEE SSST 2003, pp.400-403, 2003
4 Al-Saleh, M. A., Fast tracking two stage adaptive noise canceller, IEEE Region 10 Conference TENCON, pp.606-609, 2004
5 R. H. Kwang and E. W. Johnston, A Variable Step Size LMS Algorithm, IEEE Trans. Signal Processing, vol.40, no.7, pp.1633-1642, 1992   DOI   ScienceOn
6 Anrikulu, O. and A. G. Constantinides, The LMS algorithm with time-varying forgetting factor for adaptive system identification in additive output noise, ICASSP 96, pp.1851-1854, 1996
7 Delgado, R. E., O. Ozadmar, S. Rahman and C. N. Lopez, Adaptive noise cancellation in a multimicrophone system for distortion product otoacoustic emission acquisition, IEEE Trans. Biomedical Engineering, vol.47, no.9, pp.1154-1164, 2000   DOI   ScienceOn
8 T. Aboullnasr and K. Mayyas, A Robust Variable Step-Size LMS-Type Algorithm: Analysis and Simulations, IEEE Trans. Signal Processing, vol.45, no.3, pp.631-639, 1997   DOI   ScienceOn
9 Ho, K. C., A minimum misadjustment adaptive FIR filter, IEEE Trans. Signal Processing, vol.44, no.3, pp.577-585, 1996   DOI   ScienceOn
10 Haykin, S., Adaptive Filter Theory, 4th ed., Upper Saddle River, NJ: Prentice Hall, 2002
11 Kim, Dai I. and P. De Wild, Performance analysis of the DCT-LM2S adaptive filtering algorithm, Signal Processing, vol.80, no.8, pp.1629-1654, 2000   DOI   ScienceOn
12 Maxwell, J. A. and P. M. Zurek, Reducing acoustic feedback in hearing aids, IEEE Trans. Speech Audio Processing, vol.3, no.4, pp.304-313, 1995   DOI   ScienceOn