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http://dx.doi.org/10.7840/KICS.2011.36C.3.183

Labview FPGA Implementation of IGC Algorithm for Real Time Noise Cancelation  

Kim, Chun-Sik (대구대학교 정보통신공학과)
Lee, Chae-Wook (대구대학교 정보통신공학과)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.36, no.3C, 2011 , pp. 183-189 More about this Journal
Abstract
The LMS(Least Mean Square) algorithm is generally used because of tenacity, high mating spots and simplicity of realization. But the LMS algorithm has trade-off between nonuniform collect and EMSE(Excess Mean Square Error). To overcome this weakness, variable step size is used widely but it needs a lot of calculation load. In this paper we consider new algorithm, which can reduce calculations and adapt in case of environment changes, uses original signal and noise signal of IGC(Instantaneous Gain Control). For the real time processing of IGC algorithm, we remove the logarithmic function. The performance of proposed algorithm is tested to adaptive noise canceller in automobile. We show implemented LabVIEW FPGA system of IGC algorithm is more efficient than others.
Keywords
LMS; IGC; LabVIEW; Real-time; FPGA;
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1 Kim, Dai I. and P. De Wild, Performance analysis of the DCT-LMS adaptive filtering algorithm, Signal Processing, Vol.80, No.8, pp.1629-1654, 2000.   DOI   ScienceOn
2 Wallace, R. B. and R. A. Goubran, Improved tracking adaptive noise canceller for nonstationary environments, IEEE Trans. Signal Processing, Vol.40, No.30, pp.700-703, 1992.   DOI   ScienceOn
3 Ho, K. C., A minimum misadjustment adaptive FIR filter, IEEE Trans. Signal Processing, Vol.44, No.3, pp.577-585, 1996.   DOI   ScienceOn
4 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
5 Al-Saleh, M. A., Fast tracking two stage adaptive noise canceller, IEEE Region 10 Conference TENCON, pp.606-609, 2004.
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 Kim, Joonwan and A. D. Poularikas, Comparison of two proposed methods in adaptive noise canceling, IEEE SSST 2003, pp.400-403, 2003.
9 Liavas, A. P. and D. Tsipouridou, On the performance of the Mismatched MMSE and the LS Linear Equalizers, IEEE Trans. Acoustics, Speech, Signal Processing, Vol.55, No.7, pp.3302-3311, 2007.   DOI   ScienceOn
10 Greenberg, J. E., Modified LMS algorithms for speech processing with an adaptive noise canceller, IEEE Trans. Speech Audio Processing, Vol.6, No.4, pp.338-351, 1998.   DOI   ScienceOn
11 Haykin, S., Adaptive Filter Theory, 4th ed., Upper Saddle River, NJ: Prentice Hall, 2002.
12 Ikeda, S. and A. Sugiyama, An adaptive noise canceller with low signal distortion for speech codecs, IEEE Trans. Signal Processing, Vol.47, No.3, pp.665-674, 1999.   DOI   ScienceOn
13 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.
14 Hongyan, C., S. Chongfei, X. Xiaobo, H. Yong and K. D. Luk, Study on Adaptive Noise canceller on Fixed-Point Algorithm for Real-Time Somatosensory Evoked Potential Monitoring, ICBBE 2008, pp.3274-3277, 2008.