A Derived Robust Statistics Approach For Adaptive Volterra Filters Applied In Nonlinear Acoustic Echo Cancellation Scenarios
Journal Title: Acta Technica Napocensis- Electronica-Telecomunicatii (Electronics and Telecommunications) - Year 2013, Vol 54, Issue 1
Abstract
The paper proposes a novel updating concept for adaptive Volterra kernels that relies on a robust statistics approach. The optimization of a certain cost function leads to the update equations for the linear and quadratic kernels with respect to an optimum error threshold, set according to the relative local noise power. Thereby, for absolute error samples larger than the threshold, which occur mainly in the convergence stage of the adaptive filter, the adaptation is achieved using a sign version of the Normalized Least-Mean-Square algorithm. In the saturation stage of the filter, when most of the absolute values of the error samples are smaller than the particular threshold, the adaptation is carried out based on the Normalized Least-Mean-Square algorithm. This technique tends to eliminate the influence of large valued outliers by improving the convergence of the conventional Volterra filters and maintaining the same misadjustment in acoustic echo cancellation setups. The acoustic system is designed using measured Volterra kernels. The method is tested in terms of Echo Return Loss Enhancement for different probability density functions of the source signal. Also, for the white Gaussian noise case as excitation, a new convergence attribute is introduced for a more precise comparison of the adaptive methods.
Authors and Affiliations
Cristian CONŢAN, Marina Dana ŢOPA, Botond Sandor KIREI, Ingrid Maria KOVACS
Keywords
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