The step size parameter used in the normalized least mean square (NLMS) algorithm determines the weighting applied to the coefficient update. So the rate of convergence, the final steady-state performance and the noise insensivity of the NLMS-type adaptation algorithm are controlled by the step size parameter.
In this contribution the optimal step size parameter, which is individual for each coefficient and variant with time, for an NLMS-driven adaptation algorithm is determined in the presence of noise. Within this paper it will be shown that the resulting step size parameter equals the product of the step size parameters from two different approaches ([2, 5, 7] and [3]). The realization aspects will be discussed for an acoustic echo control application.
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