Open Access Open Access  Restricted Access Subscription or Fee Access

ADAPTIVE NOISE CANCELLATION USING NORMALIZED LMS ALGORITHM

saurabh Rai Dhirendra Prasad

Abstract


ABSTRACT
An adaptive filter is a digital filter that self adjusts its transfer function according to an optimizing algorithm. There are a number of optimizing algorithms available, but the performance, simplicity and stability of LMS algorithms outweigh other algorithms, thus LMS algorithm have become increasingly popular. The digital filter structure used in adaptive filtering is usually FIR with transversal or tapped delay line realization. Adaptive filtering is one of the core technologies in digital signal processing and finds diversified applications such as echo cancellation, channel equalization, system identification, adaptive noise cancellation, and adaptive beamforming. This paper presents Simulink implementation of an adaptive filter using normalized least mean square (NLMS) algorithms to reduce unwanted noise and thus improving signal quality.
KEYWORDS
Adaptive Filtering, Transversal Filter, Noise Cancellation, Normalized LMS Algorithm, Simulink

Full Text:

PDF

References


Farhang-Boroujeny, B. (2013) Adaptive filters: theory and applications, John Wiley & Sons.

Lee, K. A., Gan, W. S., & Kuo, S. M. (2009) Subband Adaptive Filtering: Theory and Implementation,. John Wiley & Sons.

Jiao, Y., Cheung, R. Y., Chow, W. W., & Mok, M. P. (2014) “Signed-gradient adaptive step size LMS algorithm for biomedical applications”, IEEE 36th Annual International Conference in Engineering in Medicine and Biology Society (EMBC), pp. 3208-3211.

Marshall, D. F., & Jenkins, W. K. (1992) “A fast quasi-Newton adaptive filtering algorithm”, IEEE Transactions on Signal Processing, Vol. 40, No. 7, pp1652-1662.

Breining, C. et al. (1999) “Acoustic echo control. An application of very-high-order adaptive filters”, IEEE Signal Processing Magazine, Vol. 16, No. 4, pp42-69.

Tüchler, M., Singer, A. C., & Koetter, R. (2002) “Minimum mean squared error equalization using a priori information”, IEEE Transactions on Signal Processing, Vol. 50, No. 3, pp673-683.

Widrow, B. et al. (1975) “Adaptive noise cancelling: Principles and applications”, Proceedings of the IEEE, Vol. 63, No. 12, pp1692-1716.

Chang, C. Y., Pan, S. T., & Liao, K. C. (2013) “Active noise control and its application to snore noise cancellation”, Asian Journal of Control, Vol.15, No.6, pp1648-1654.

Zhao, S. (2009) Performance analysis and enhancements of adaptive algorithms and their applications, Doctoral dissertation, Nanyang Technological University.

Harris, R. W., Chabries, D. M., & Bishop, F. A. (1986) “A variable step (VS) adaptive filter algorithm”, IEEE Transactions on Acoustics, Speech and Signal Processing, Vol.34, No.2, pp309-316.

Górriz, J. M., Ramírez, J., Cruces-Alvarez, S., Puntonet, C. G., Lang, E. W., & Erdogmus, D. (2009) “A novel LMS algorithm applied to adaptive noise cancellation”, IEEE Signal Processing Letters, Vol. 16, No. 1, pp34-37.

Lázaro-Gredilla, M., Azpicueta-Ruiz, L., Figueiras-Vidal, A. R., & Arenas-García, J. (2010) “Adaptively biasing the weights of adaptive filters”, IEEE Transactions on Signal Processing, Vol. 58, No. 7, pp3890-3895.

Haykin, S. S. (2008) Adaptive filter theory, Pearson Education India.

Bershad, N. J. (1986) “Analysis of the normalized LMS algorithm with Gaussian inputs”, IEEE Transactions on Acoustics, Speech and Signal Processing, Vol.34, No.4, pp793-806.




DOI: https://doi.org/10.37628/ijtet.v1i2.130

Refbacks

  • There are currently no refbacks.