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A SQUARE ROOT NLMS ALGORITHM FOR ADAPTIVE IDENTIFICATION WITH SPEECH INPUT

This paper, presents a new Normalized Least Mean Square (NLMS) algorithm, tailored for adaptive identification of invariant systems impulse responses with speech inputs. The proposed Square Root NLMS (SR-NLMS) algorithm is based on a specific normalization of the LMS adaptive filter input, by the Euclidean norm of the tap-input. In fact, we cancel the term involving the statistics of the input signal that amplifies the observation noise power in the misadjustment time evolution during low dynamics of the input. Consequently, the robustness of the LMS adaptive filter with respect to the time-varying input signal statistics is significantly enhanced. This is confirmed through an experimental study that displays the attractive steady state performances of the SR-NLMS algorithm. A comparison with the classical NLMS algorithm shows that, the Mean Square Deviation is decreased with the SR-NLMS algorithm.