Published In : IJCSN Journal Volume 5, Issue 1

Date of Publication : February 2016

Pages : 165-170

Figures :09

Tables : 04

Publication Link : A Novel Technique for Speech Processing Using Combinational Adaptive Methods

 

 

 

M. Koteswara Rao : obtained M.Tech from JNTUK, Kakinada pursuing PhD from JNTU kakinada. He is having an experience more than 15 years and also having more number of both national and international journals, conferences. His area of interest is speech signal processing, presently working as associate professor in Sri Vasavi Engineering College, pedatadepalli, Tadepalligudem, A.P.

Dr. I. Santi Prabha : is working as Professor in ECE Department in Jawaharlal Nehru technological university ,kakinada, She did her B.Tech & M. Tech with specialization in Instrumentation and Control Systems from JNTU College of engineering, Kakinada. She was awarded with Ph.D. in Speech signal processing by Jawaharlal Nehru Technological University in 2005. She has 29 years experience of teaching to UG and PG classes and has received good response from the students. She has also 15 years of Research experience. She has produced two Ph.D. and guiding 7 Ph.D scholars. She has guided more than 50 M.Tech. projects. She is a member of I.S.T.E. and fellow member in I.E.T.E.& I.E. She has published 40 technical papers in national and International journals and conferences. She worked as Head of ECE Department and Deputy Warden for Lady’s hostel, JNTUK, KAKINADA,

 

 

 

 

 

 

 

Windows

FIR

Combinational LMS Algorithm

Shadow Technique

The Implementation of Adaptive-FIR Filter using shadow concept for Kaiser window was performed. we applied a noised speech signal to Adaptive filter and obtained de noised wave form at output which is shown in Fig-3. Later We shown responses of shadow based Adaptive filter from Fig:4 to Fig:6 for different shadow factors of the Normalized LMS algorithm. Similarly Fig:7 to Fig:9 for different shadow factors of the variable ste-sized Least mean square algorithm algorithm. Finally We compared SNR, mean square error(MSE) at input and Output which are shown from Table-1.,table-3 Respectively for Normalized LMS algorithm table-2 and Table-4 for variable step-sized Least mean square algorithm. From the above discussions it is concluded that shadow based adaptive filter produces better responses in terms of SNR and MSE compared to Normalized and variable step- sized LMS algorithm.

 

 

 

 

 

 

 

 

 

[1] Zhipei Chi, Student Member, IEEE, Jun Ma, Member, IEEE, and Keshab K. Parhi, Fellow, IEEE,’’ Hybrid Annihilation Transformation (HAT)for Pipelining QRD-Based Least- SquareAdaptive Filters’’,ieee transactions on circuits and systems—ii: analog and digital signal processing, vol. 48, no. 7, july 2001 661 [2] Daniel J. Allred, Student Member, IEEE, HeejongYoo, Student Member, IEEE, Venkatesh Krishnan, Student Member, IEEE, Walter Huang, Student Member, IEEE, and David V. Anderson, Senior Member, IEEE.’’ LMS Adaptive Filters Using Distributed Arithmetic for High Throughput’’,ieee transactions on circuits and systems—i: regular papers, vol. 52, no. 7, july 2005 1327 [3] Sen M. Kuo, Senior Member, IEEE, and Hsien-Tsai Wu, Member, IEEE,’’ Nonlinear Adaptive Bilinear Filters for Active Noise Control Systems’’,ieee transactions on circuits and systems—i: regular papers, vol. 52, no. 3, march 2005 617 [4] Emilio Soria, Javier Calpe, Member, IEEE, Jonathon Chambers, Senior Member, IEEE, MarcelinoMartínez,Gustavo Camps, Member, IEEE, and José David Martín Guerrero,’’ A Novel Approach to Introducing Adaptive Filters Based on the LMS Algorithm and Its Variants’’, ieee transactions on education, vol. 47, no. 1, February 2004 127 [5] Yonggang Zhang and Jonathon A. Chambers, Senior Member, IEEE,’’ Convex Combination of Adaptive Filters for a Variable Tap-Length LMS Algorithm’’,ieee signal processing letters, vol. 13, no. 10, october 2006 [6] Bijit Kumar Das and MrityunjoyChakraborty, Senior Member, IEEE,’’ Sparse Adaptive Filtering by an Adaptive Convex Combination of the LMS and the za-lms algorithm’’,ieee transactions on circuits and systems—i: regular papers, vol. 61, no. 5, may 2014 1499 [7] Márcio H. Costa, José Carlos M. Bermudez, Member, IEEE, and Neil J. Bershad, Fellow, IEEE,’’ Stochastic Analysis of the LMS Algorithm with a Saturation Nonlinearity Following the Adaptive FilterOutput’’,ieee transactions on signal processing, vol. 49, no. 7, july 2001 [8] Boaz Rafaely, Member, IEEE, and Stephen J. Elliott, Senior Member, IEEE,’’ A Computationally Efficient Frequency-DomainLMS Algorithm with Constraints on theAdaptive Filter’’,ieee transactions on signal processing, vol. 48, no. 6, june 2000 [9] Shin’ichi Koike, Member, IEEE,’’ Analysis of Adaptive Filters UsingNormalized Signed Regressor LMS Algorithm’,ieee transactions on signal processing, vol. 47, no. 10, october 1999’ [10] Jerónimo Arenas-García, Member, IEEE, Vanessa Gómez-Verdejo, andAníbal R. Figueiras-Vidal, Senior Member, IEEE,’’ New Algorithm for Improved Adaptive Convex Combination of LMS Transversal Filters’, New Algorithm for Improved Adaptive Convex Combination of LMS Transversal Filters’,ieee transactions on instrumentation and measurement, vol. 54, no. 6, december 2005 2239 [11] Y. Zhou, S. C. Chan, and K. L. Ho,’’ A New Block- Exact Fast LMS/Newton Adaptive Filtering Algorithm’’, IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 54, NO. 1, JANUARY 2006 [12] ibtissamconstantin,regislengelle-“performance analysis of kernal adaptive filters based on lms algorithm”-procedia computer science 20(2013) 39- 45,sciencdirect. [13] michael a. vaudrey, william t. baumann, william r. saunders-“stability and operatingconstraints of adaptive lms-based feedback control”-automaticssciencdirect [14] wang an-dong, liulan ,weiqin-“an adaptive morphologic filter applied to ecg de-noising and extraction of r peak at real-time” 2012 aasri conference on computational intelligence and bioinformatics,elsevier [15] kaibohu*, yaxuanliu-“adaptive noise cancellation method for fiber optic gyroscope”- 2012 international workshop on information and electronics engineering (iwiee),elsevier [16] p.v.muralidhar, v. l. n.sastry d, s.k.nayak, “interpretation of dirichlet,bartlett, hanning and hamming windows using fractional fouriertransform”, international journal of scientific & engineering research,vol. 4, issue 6, june-2013. [17] muralidhar, p. v. DVLN sastry, and s.k.Nayak. "spectral analysis of shadow window-fir filters." int. conf. on advances in communication, network, and computing. 2013.] [18] p.v.muralidhar,a.s.rao,s.k.nayak,”spectral interpretation of sinusoidal wave using fractional fourier transform based fir window functions”- international review of computers and software 4.6(2009). [19] Muralidhar.p.v.,a.s.srinivasarao,dr.s.k.nayak” fractional fourier transform based fir window functions”-proceedings of IIndinterrnational conference rsps-2010 sponsored by ieee(hyderabad section). [20] MuralidharP.V, D.Natraj, V.LokeshRaju and S.K.nayak “Implementation of different FIR high pass filters using Fractional Kaiser window” 2010 IEEE International Conference ,vol:2,pp 651 [21] N.Mohana Rao, A.S.Srinivasa Rao, P.V.Muralidhar , Venkata L N Sastry.D ,’’ Noise Removal in Cardiac Signal by Shadow Digital Filters’’, International Journal of Engineering Research & Technology (IJERT)Vol. 2 Issue 10, October – 2013,issn:2278-0181 [22] Y. Lakys and A. Fabre,’’ Shadow filters – new family of second-order filters’’, ELECTRONICS LETTERS 18th February 2010 Vol. 46 No. 4 [23] S.C. Dutta Roy,’’ ‘Shadow’ Filters - A New Family of lectronically Tunable Filters’’, IETE Journal of Education Publication details, including instructions for authors and subscription information :http://www.tandfonline.com/loi/tije20.