System Identification and Echo Canceller with Adaptive Filtering Algorithms

B.Anitha*, Srinivas Bachu**, C.Sailaja***
* Associate Professor, Department of Electronics and Communication Engineering, GNITC, Telangana, India.
** Research Scholar, Department of Electronics and Communication Engineering, GITAM University, Telangana, India.
*** Assitant Professor, Department of Electronics and Communication Engineering, GNITC, Telangana, India.
Periodicity:July - September'2015
DOI : https://doi.org/10.26634/jdp.3.3.3593

Abstract

The primary objective of this paper is to present a simulation scheme to simulate an adaptive filter using Least Mean Square, and Normalized Least Mean Square adaptive filtering algorithms for system identification and echo cancellation. The objective of echo cancellation is to estimate the unknown system response that is system identification. With the help of system identification and adaptive filtering algorithms Mean Square Error (MSE) can be minimized and hence echo free signal can be obtained. This method uses a primary input signal that contains speech signal and a reference input signal containing noise. The estimated signal is obtained by subtracting adaptively filtered reference input signal from the primary input signal. In this method, the desired signal corrupted by an additive echo can be recovered by an adaptive echo canceller using LMS, and NLMS algorithms. This adaptive echo canceller is useful in minimizing the MSE and to improve the SNR. Here the estimation of the adaptive filtering is done using MATLAB environment.

Keywords

Adaptive Filters, Signal-Noise Ratio, Mean Square Error, Least Mean Square, and Normalized Least Mean Square

How to Cite this Article?

Anitha.B., Bachu,S., and Sailaja.C. (2015). System Identification and Echo Canceller with Adaptive Filtering Algorithms. i-manager's journal on Digital Signal Processing, 3(3), 30-34. https://doi.org/10.26634/jdp.3.3.3593

References

[1]. Chinaboina, Radhika, et al. (2011). "Adaptive Algorithms For Acoustic Echo Cancellation In Speech Processing”, International Journal of Research & Reviews in Applied Sciences 7.1.
[2]. Soria, E.; Calpe, J.; Chambers, J.; Martinez, M.; Camps, G.; Guerrero, J.D.M.; (2008). “A novel approach to intro-ducing adaptive filters based on the LMS algorithm and its variants”, IEEE Transactions, Vol. 47, pp. 127-133.
[3]. Eneman, K.; Moonen, M.; (2003). “Iterated partitioned block frequency-domain adaptive filtering for acoustic echo cancellation”, IEEE Transactions on Speech and Audio Processing, Vol. 11, pp. 143-158.
[4]. G. Egelmeers, P. Sommen, and J. de Boer, (1996). “Realization of an acoustic echo canceller on a single DSP”, Eur. Signal Processing Conf. (EUSIPCO96), Trieste, Italy, pp. 33–36.
[5]. J. Shynk, (1992). “Frequency-domain and multirate adaptive filtering”, IEEE Signal Processing Mag., Vol. 9, pp. 15– 37.
[6]. Ahmed I. Sulyman and Azzedine Zerguine, (2004). “Echo Cancellation Using a Variable Step-Size NLMS Algorithm”, Eupisco, Electrical and Computer Engineering Department Queen's University, pp. 401-404.
[7]. D. L. Duttweiler, (1978). “A twelve-channel digital echo canceller”, IEEE Trans. Commun., Vol. 26, No. 5, pp. 647–653.
[8]. Shi, Kun, Xiaoli Ma, and G. Tong Zhou. (2009). “An efficient acoustic echo cancellation design for systems with long room impulses and nonlinear loudspeakers”, Signal Processing, 89.2, pp. 121-132.
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