Alaka Barik
ABSTRACT
In this paper, a multiple sub-filter (MSF) approach is discussed in which, a single long filter (SLF) is partitioned into multiple subfilters to achieve fast convergence rate. The performance of the MSF parallel structure adaptation is studied for common error and different error modes using least mean square (LMS) adaptive algorithm. Simulation results show that the MSF structure provides better convergence over the SLF for both error signals. However, the steady state error performance of the different error adaptation algorithm (DEA) is poor as compared to that of common error adaptation algorithm (CEA) as well as that of the SLF adaptation algorithm. In order to achieve a trade-off between steady state error and convergence speed, a combination of both the algorithms is studied and is named as combined error adaptation algorithm (COMBEA). Further to reduce the computational load of updating all coefficients, called full update (FU) algorithm, of the MSF and SLF, a scheme named as selective coefficient update (SCU) algorithm is proposed in which only few coefficients are updated at each iteration. Finally the tracking performance of the MSF and SLF for time-varying acoustic channel is demonstrated.
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Cross Ref
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- A. Barik, G. Murmu, T. P. Bhardwaj, and R. Nath, "LMS adaptive multiple sub-filters based acoustic echo cancellation", Proc. 2010 IEEE Int. Conf. Computer and Communication Technology (ICCCT'10), Allahabad, India, pp. 824--827, Sept., 2010.Google ScholarCross Ref
Index Terms
- A family of multiple sub-filters based acoustic echo cancellers
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