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Wavelet Based Adaptive Filtering Algorithms for Acoustic Noise Cancellation

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This paper prefers Acoustic Noise cancellation (ANC) system using Wavelet based adaptive filtering algorithms. The Acoustic Noise canceller is implemented using adaptive algorithms like LMS (Least Mean Square), NLMS (Normalized Least Mean Square),RLS (Recursive Least Square), and FRLS (Fast Recursive Least Square). The inclusion of wavelet based transformation in ANC reduces the number of samples to be processed and increase the efficiency of the system by minimizing the processing time. The simulation results shows that the wavelet transform based adaptive algorithms produce improvement in SNR (Signal to Noise Ratio) with less execution time compared to conventional adaptive algorithms.
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Acoustic Noise Cancellation; Least Mean Square; Normalized Least Mean Square; Recursive Least Square; Fast Recursive Least Square; Signal to Noise Ratio

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S. Haykin, Adaptive Filter Theory (Prentice Hall, 2002).

A. H. Sayed, Fundamentals of Adaptive Filtering (Wiley, 2008)

B. Widrow, S. Steam, Adaptive Signal Processing (Prentice Hall, 1985.)

D. T. M. Slock, on the convergence behaviour of the LMS and the NLMS algorithms. IEEE Trans. Signal Processing, Vol. 42, pp. 2811-2825, 1993

S. I. A. Sugiyama, An adaptive noise canceller with low signal distortion for speech codes. IEEE Trans. Signal Processing, Vol. 47, n. 3, pp. 665-674, 1999.

M. S. E. Abadi, J. H. Husoy, and A.M. Far, Convergence analysis of two recently introduced adaptive filter algorithms (FEDS/RAMP). Iranian Journal of Electrical and Computer Engineering (IJECE), Vol. 7, n. 1, 2008.

M. S. E. Abadi, J. H. Husoy, A comparative study of some simplified RLS type algorithm. Proc. Intl. Symp on control, Communications and Signal Processing, Hammamet, Tunisia, (Year of Publication: 2004)

M. Arezki, A. Beneallal, Error Propagation Analysis of Fast Recursive Least Squares Algorithms. Proc. 9th IASTED International Conference on Signal and Image Processing, Honolulu, Hawaii, USA, Vol. 20–22, n. 8, pp .97-101, 2007.

G. V. Moustakides, and V. Theodoridis, Fast Newton transversal filters - A new class of adaptive estimation algorithms. IEEE Trans. Signal Process, Vol. 39, n. 10, pp. 2184–2193, 1991.

D. Mansour and A. H. Gray Jr., “Unconstrained frequency domain filter”, IEEE Trans. Acoust., Speech, Signal Processing, Vol. ASSP-30, n. 5, pp. 726-734,Oct. 1982.

M. Dentino, J. McCool, and B. Widrow, “Adaptive filtering in frequency domain”, Proc. IEEE. Vol. 66, n. 12, pp. 1658-1659, Dec. 1978.

S. S. Narayan, A. M. Peterson, and M. J. Narasimha, “Transform-domain LMS algorithm”, ”, IEEE Trans. Acoust., Speech, Signal Processing, Vol. ASSP-31, n. 3, pp. 609-615, June. 1983.

D. I. Kim and P. De Wilde, “Performance analysis of the DCT-LMS adaptive filtering algorithm”, Signal Processing, Vol. 80, n. 8, pp. 1629-1654, Aug. 2000.

R. C. Bilcu, P. Kuosmnen, and K. Egiazarian, “A transform domain LMS adaptive filter with variable step-size”, IEEE Trans., Signal Processing, Vol.9, n .2, Feb 2002.

B. Widrow, J. R. Glover, et al., “Adaptive noise cancelling: Principles and applications”, Proc. IEEE, pp. 1692-1716, Dec. 1975.

R. W. Harris, D. M. Chabries, and F. A. Bishop, “A variable step (vs) adaptive filter algorithm”, IEEE Trans., Acoustic, speech, Signal Processing, ASSP-34, pp. 309-316

T. J. Shan and T, Kailath, “Adaptive algorithms with an automatic gain control feature”, IEEE Trans. Circuits Syst., Vol. CAS-35, pp. 122-127, Jan. 1988.

Ramli, R., Abid Noor, A., Abdul Samad, S., Modified Adaptive Line Enhancer in Variable Noise Environments using Set-Membership Adaptive Algorithm, (2014) International Review on Computers and Software (IRECOS), 9(8), pp. 1468-1475.

Yue Wang, Chun Zhang, and Zhihua Wang, “A new variable step-size LMS algorithm with application to active noise control”, IEEE International conference on Acoustic speech and signal processing (ICASSP), china, 2003, 6-10 April.

C. S. Burrus, et al., Introduction to Wavelet and Wavelet Transforms (Prentice Hall Inc., 1998).

I. Y. Soon, S. N. Koh, and C. K. Yeo, Wavelet for speech denoising. Proc. of the IEEE TENCON, pp. 479-482, 1997.

V. K. Gupta, M. Chandra, and S. N. Sharan, Acoustic Echo and Noise cancellation system for Hand free telecommunication using variable step size algorithms. Radio engineering, Vol. 22, no. 1, p. 200-207, 2013.

N. Ramesh Babu, and P. Arulmozhivarman, Improving Forecast Accuracy of Wind Speed Using Wavelet Transform and Neural Networks. J Electrical Engg Technology, Vol. 8, n. 3, pp. 559-564, 2013.


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