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Reseach Article

Fractional Derivative based Echo Cancellation for Enhancement of Voice Quality

by Abid Khan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 14
Year of Publication: 2020
Authors: Abid Khan
10.5120/ijca2020920609

Abid Khan . Fractional Derivative based Echo Cancellation for Enhancement of Voice Quality. International Journal of Computer Applications. 175, 14 ( Aug 2020), 7-9. DOI=10.5120/ijca2020920609

@article{ 10.5120/ijca2020920609,
author = { Abid Khan },
title = { Fractional Derivative based Echo Cancellation for Enhancement of Voice Quality },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2020 },
volume = { 175 },
number = { 14 },
month = { Aug },
year = { 2020 },
issn = { 0975-8887 },
pages = { 7-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number14/31519-2020920609/ },
doi = { 10.5120/ijca2020920609 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:25:01.071272+05:30
%A Abid Khan
%T Fractional Derivative based Echo Cancellation for Enhancement of Voice Quality
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 14
%P 7-9
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Echo cancellation and echo suppression are the methods to improve the voice quality. For echo cancellation the right path of echo is necessary. When we transmit a sound signal it is severely effected by echo. In this research paper focus is given to fractional derivative based adaptive strategies for echo cancellation. Because the overall performance of fractional derivative based approach is better than other conventional methods of echo cancellation. Other conventional algorithms for echo cancellation are LMS (least mean square) , RLS (recursive least square), and NLMS (normalized least mean square). Where as FNMLS (fractional normalized least mean square) is fractional derivative based method. Therefore, we will exploit this method to improve the performance of echo cancelation algorithm. Various mathematical rule and methods used for fractional derivative based echo cancellation are Taylor series ,Grunawld letnikove method, Roy method , Matsuda method, Riemann Liouville formula and L hopital rule. In this research work I will concentrate on fractional derivative based approach for echo cancellation. General Term Echo cancellation , Fractional derivative based approach, Interlacing , Radwan procedure

References
  1. Fractional normalized filter error least mean square algorithm for application in active noise control. Shah, S.M Samar,Raja M.A.Z. 2014, pp. 973-975.
  2. A new adaptive stratgy to improve online secondary path modeling in active noise control system using fractional signal processing approach . Aslam, M.S,Raja ,M.A.Z. 2014, pp. 433-443.
  3. Fractional order constant modulus blind algorithm with application to channel equalization. Shah, S.M, Samar, Naqvi, S.M.R. 2014, pp. 1702-1704.
  4. Acoustic Signal processing for telecommunication. banesty, S.Gay and J. 2000, IEEE.
  5. Nonlinear echo cancellation for hand free speaker phone . Jones, B.S .Nollert D.L. 1997, IEEE.
  6. Study of the optimal simplified Kalman Filter for echo cancellation. C.Paleologu, J banesty S. Cicochin. 2013, IEEE, pp. 580-584.
  7. Study of the optimal simplified filter for echo cancellation. S.Ciochin, C.Paleolug J.Benesty. 2013, IEEE Trans. , pp. 1539-1549.
  8. State space frequency domain adaptive filtering for non linear acoustic echo cancellation . Enzner, S.Malik and G. 2012, IEEE , pp. 2065-2079.
  9. Adaptation of acoustic echo canceller incorporating a memoryless nonlinearity . A.Stenger, W.Kellermann , Rabenstein. 1999.
  10. Compensation of loudspeaker nonlinearity in acoustic echo cancellation using raised cosine type equation . W.Zhu, H.Dai and. 2006, IEEE, pp. 1190-1194.
  11. Nonlinearity acoustic echo cancellation using adaptive orthoganalized power filter . F.Kuech, A.Mitnacht and W.Kellermann. 2005, IEEE, pp. 105-108.
  12. Statistical and adaptive signal processing spectral estimation signal modeling adaptive filtering and array processing. D. Manolakis, V Angel and S.Kogon. 2005, IEEE.
  13. Novel variable step size nlms algorithm for echo cancellation. S.Grant, M. Iqbal and. 2008, IEEE, pp. 241-244.
  14. Adaptive filter theory. S.Haykin. 1996, Prentice Hall .
  15. Signal system. Verges, A.V Oppenheim and G.C. 2010.
  16. A new approach to linear filtering and prediction problem . Kalman, R.E. 2006, ASME Journal of basic engineering , pp. 35-45.
  17. Study of the general kalman filter for echo cancellation. S.Ciochina, C. Paleologu J Benesty and. 2013, IEEE, pp. 1539-1549.
  18. Experiement with sub band acoustic echo cancellers for teleconferencing . Gillorie, A. IEEE, 1998, pp. 2141-2144.
  19. Analysis and design of multi rate system for echo cancellation of acoustic echo. W.Kellermann. 2008, ICASP , pp. 2570-2573.
  20. A real time implementation of a sterphonic acoustic echo canceller . P.Eneroth, S.Gay , T gansler and J. Banesty. 2001, IEEE, pp. 513-523.
  21. Advances in network and acoustic echo cancellation . J.Benesty, T.G Ansler , D.R Morgan , M. Sondhi. 2010, Springer.
  22. Weaver SSB sub band Acoustic Echo canceller. L.Chu, Peter. 2010, IEEE.
  23. A new adaptive algorithm for sterophonic acoustic echo canceller. Y. Jung . J. Lee, Y Park , Due-Hee Youn. 2000, IEEE.
  24. Combined acoustic echo and noise reduction using GVSD-based optimal filtering . D.Simon, M.Moonen and C.Erik. 2000, IEEE.
  25. A Psychoacoustic approach to combined acoustic echo cancellation and noise reduction. al, S.Gustafsson et. IEEE, IEEE, p. 2002.
  26. Acoustic echo cancellation using digital signal processing . H.Michael. November 2003, IEEE.
  27. A blind approach to joint and acoustic echo cancellation. N.Sven, Y.Siow and. 2005, IEEE.
  28. Acoustic echo cancellation and double talk detection using estimated loudspeaker impulse response. P.Ahgren. 2005, IEEE.
  29. Low complexity adaptive filtering implementation for acoustic echo cancellation. al, S.Christian et. 2006, IEEE.
  30. Efficient multichannel NLMS implementation for acoustic echo cancellation. L.Fredric. 2007, IEEE.
  31. Improving the convergence of the NLMS algorithm using constrained sub band update. S.Gan, Kong A Lee and Woon. IEEE, pp. 736-739.
  32. Non uniform subband adaptive filtering with critical sampling. Batalherio, Petergalia M.R and P. 2008, IEEE, pp. 565-575.
  33. Alias free sub band adaptive filtering with critical sampling . Kim S.G, CD Yoo and T.Q Nguyen. 2008, IEEE , pp. 1894-1904.
  34. Filter bank design for subband adaptive filtering structure with critical sampling . Batalherio, Petrgalia M.R and PB. 2004, IEEE, pp. 1194-1202.
  35. Proportionate normalized sub band adaptive filter algorithm for sparse System identification. Abadi, Mohammad Shams Esfand. 2009, pp. 1467-1474.
  36. Proportionate normalized adaptation in echo cancellers. Duttweiler, Donald L. 2000, IEEE, pp. 508-518.
Index Terms

Computer Science
Information Sciences

Keywords

LMS NLMS RLS FNLMS ERLE PDES