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Free Inter-band Aliasing Sub-band Adaptive Filtering with Critical Sampling Filter bank Analysis

by Telagarapu.Prabhakar, Dr.K.Satya Prasad, P.M.K. Prasad
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 11
Year of Publication: 2011
Authors: Telagarapu.Prabhakar, Dr.K.Satya Prasad, P.M.K. Prasad
10.5120/1731-2341

Telagarapu.Prabhakar, Dr.K.Satya Prasad, P.M.K. Prasad . Free Inter-band Aliasing Sub-band Adaptive Filtering with Critical Sampling Filter bank Analysis. International Journal of Computer Applications. 12, 11 ( January 2011), 6-10. DOI=10.5120/1731-2341

@article{ 10.5120/1731-2341,
author = { Telagarapu.Prabhakar, Dr.K.Satya Prasad, P.M.K. Prasad },
title = { Free Inter-band Aliasing Sub-band Adaptive Filtering with Critical Sampling Filter bank Analysis },
journal = { International Journal of Computer Applications },
issue_date = { January 2011 },
volume = { 12 },
number = { 11 },
month = { January },
year = { 2011 },
issn = { 0975-8887 },
pages = { 6-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume12/number11/1731-2341/ },
doi = { 10.5120/1731-2341 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:01:22.126755+05:30
%A Telagarapu.Prabhakar
%A Dr.K.Satya Prasad
%A P.M.K. Prasad
%T Free Inter-band Aliasing Sub-band Adaptive Filtering with Critical Sampling Filter bank Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 12
%N 11
%P 6-10
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Adaptive Filtering is an important concept in the field of signal processing and has numerous applications in fields such as speech processing and communications. Examples in speech processing include speech enhancement, echo and interference cancellation and speech coding. An Adaptive filter is a filter that self-adjusts its transfer function according to an optimizing algorithm. Because of the complexity of the optimizing algorithms, most adaptive filters are digital filters that perform digital signal processing and adapt their performance based on the input signal. An Adaptive filter is often employed in an environment of unknown Statistics for various purposes such as system identification, inverse modeling for channel equalization, adaptive prediction and interference canceling. Knowing nothing about the environment , the filter is initially set to an arbitrary condition and updated in a step by step manner towards an optimum filter setting. For updating, the least mean-square algorithm is often used for its simplicity and robust performance. However , the L MS algorithm exhibits slow convergence when used with an ill-conditioned input such as speech and requires a high computational cost, especially when the system to identified has a long impulse response. Simulations show that the proposed structure converges faster than both an equivalent full band structure at lower computational complexity and recently proposed SAF structures for a colored input. The analysis is done using MATLAB, a language of technical computing, widely used in Research, Engineering and Scientific computations.

References
  1. Adaptive Filter Theory by Simon Haykin,Pearson.
  2. Discrete-Time Signal Processing by A. V. Oppenheim and R. W. Schafer,PHI.
  3. Digital Signal Processing: Principles, Algorithms, and Applications by J. G. Proakis and D. G. Manolakis,PHI
  4. MATLAB7-Rudra Pratap, Oxford University Press.
  5. B. Widrow and S. D. Stearns, Adaptive Signal Processing, Second Edition, Pearson.
  6. Digital Signal Processing- Sanjit K.Mitra, Second Edition.
  7. MATLAB Programming- Y.Kirani Singh,B.B.Chaudhuri.
  8. SangGyun Kim, Chang D. Yoo, and Truong Q. Nguyen “Alias-Free Subband Adaptive Filtering With Critical Sampling” IEEE Transactions On Signal Processing, Vol. 56, No. 5, May 2008,pp.1894-1904.
  9. SangGyun Kim, Chang D. Yoo, and Truong Q. Nguyen “Alias-Free Subband Adaptive Filtering With Critical Sampling” IEEE Transactions On Signal Processing, Vol. 56, No. 5, May 2008,pp.1894-1904.
  10. Christian Schüldt,Fredric Lindstrom, and Ingvar Claesson “A Low-Complexity Delayless Selective Sub band Adaptive Filtering Algorithm” IEEE Transactions On Signal Processing, Vol. 56, No. 12, December 2008
Index Terms

Computer Science
Information Sciences

Keywords

Adaptive filtering aliasing Critical Sampling LMS Algorithm.QMF