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Hardware Implementation of Fast Transversal Least Mean Square Algorithm in Acoustics, Speech, and Signal Processing (ASSP) Using TMS320C5X Processor

J. Jebastine, Dr.B. Sheela Rani

Abstract


This paper describes the Normalized Least Mean Square algorithm and Fast Transversal Least Mean Square Algorithm for effective noise cancellation. The Simulink model for NLMS and Fast Transversal LMS Algorithm had been designed which results in a noise free signal as output in ASSP. The filter used here is adaptive filter and the algorithm used is Least Mean Square algorithm and Fast Transversal Least Mean Square Algorithm. The input given is the original speech signal/sinusoidal varying input where in white Gaussian noise / Random noise are deliberately introduced to the block. By varying the adaptive step size, Signal to Noise Ratio is determined and are compared for both the algorithms. Based on these results the optimum step size is found for noise free output and the best efficient algorithm is identified. The Fast Transversal LMS algorithm is found to be a suitable solution for adaptive filtering applications and hence chosen for implementation in hardware using TMS320C5X processor. Thus hardware has been implemented for effective removal of noise in audio and speech processing and it can be widely used in the detection of Narrow band signals in Broad band Noise.

Keywords


Adaptive Filter, ASSP, FT-LMS and N-LMS, Tms320c5x Processor.

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References


Zhou.Y (2004), „A New Block Exact FAST / Newton Adaptive Algorithm‟, 47 th IEEE International on Circuits and Systems, Pg. 29-32.

J.Jebastine ,B.SheelaRani ,June(2012) ,‟Design and Implementation of Noise Free Audio Speech Signal Using Fast Block Least Mean Square Algorithm‟, „Signal and Image Processing :International Journal(SIPIJ)‟Volume 3, No.3.

Sanaullah Khan .M (2004), „Comparison of LMS, RLS and Notch Based Adaptive Algorithm for Noise Cancellation of a typical industrial work room‟, IEEE Transactions on Speech Signal Processing ,Pg. 169-173

Ali.M.Millani (2007), „Performance Analysis of Sub-Band NLMS, Apa and RLS InFmri ANC with A Non-Minimum Phase Secondary Path‟. IEEE Transactions on ASSP2007 ,Pg. 353-356

Berberidis.k and Theodoridis.S (Jan. 1999), “A New Fast Block Adaptive Algorithm,” IEEE Trans. Signal Processing, vol. 47, No. I , pp. 75-87.

Ferrara.E.R (Aug. 1980), „Fast implementation of LMS adaptive filters‟ IEEE Trans. Acoustic., Speech, Signal Processing, vol. ASSP-28, pp. 474-475.

Mou.Z.J (Sept. 1989.), „Fast FIR filtering: algorithms and architectures‟, Ph.D. Dissertation, University of Paris, Sud, Paris, France.

Proakis J.G., Rader.C.M Ling.F, C. L. Nikias, M. Moonen, and I. K. Proudler (2002). „Algorithms for Statistical signal Processing‟. Prentice Hall Press, New Jersey.

Widrow.B and McCool.J.M (Sept. 1976), „A comparison of adaptive algorithms based on the methods of steepest descent and random search ‟IEEE Trans. Antennas Propagation‟. Vol. AP-24, pp. 616-637.

B.Venkataramani & M. Bhaskar (2002), „Digital Signal Processor Architecture, Programming and Application‟, TMH.

Avtar singh, S.Srinivasan (2003),‟DSP Implementation using DSP microprocessor with Examples from TMS32C54XX‟, Thamson / Brooks‟s cole Publishers.

Freire, N.L. SRI Int., Menlo Park, CA, USA Douglas, S.C.( April 1993) Adaptive cancellation of geomagnetic background noise using a sign-error normalized LMS algorithm , „IEEE International Conference on Acoustics, Speech, and Signal Processing‟, 523 - 526 vol.3.

S. M. Kuo and D. R. Morgan,(1996),‟ Active Noise control Systems, algorithms and DSP implementations‟, Wiley, US.

R. H. Kwong and E. W. Johnston, (July 1992), “A Variable step-size LMS algorithm,‟ IEEE Transactions on Signal Processing‟, vol. 40, no. 7, pp. 1633-1642.

B. F. Boroujeny, Aug(1997) „Fast LMS/Newton Algorithms Based on Autoregressive Modeling and Their Application to Acoustic Echo Cancellation‟, IEEE Trans. on Signal Processing. vol. 45, NO. 8.

Kun Shi, Member, IEEE, and Xiaoli Ma, Senior Member, IEEE Feb (2010), A Frequency Domain Step-Size Control Method for LMS Algorithms, IEEE SIGNAL PROCESSING LETTERS, VOL. 17, NO. 2.

S. Makino, Y. Kaneda, and N. Koizumi,( Jan. 1993), “Exponentially weighted step size NLMS adaptive filter based on the statistics of a room impulse response,” IEEE Trans. Speech Audio Process., vol. 1, pp. 101–108.

D. R. Morgan and S. G. Kratzer, (Aug. 1996), “On a class of computationally efficient, rapidly converging, generalized NLMS algorithms,” IEEE Signal Process. Letter, vol. 3, pp. 245–247.


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