Perfect-sweep NLMS for time-variant acoustic system identification

摘要

Fast and robust acoustic system identification is still a research topic of interest, because of the typically time-variant nature of acoustic systems and the natural performance limitation of electroacoustic measurement equipment. In this paper, we propose NLMS-type adaptive identification with perfect-sweep excitation. The perfect-sweep is derived from the more general class of perfect sequences and, thus, it inherits periodicity and especially the desired decorrelation property known from perfect sequences. Moreover, the perfect-sweep shows the desirable characteristics of swept sine signals regarding the immunity against non-linear loudspeaker distortions. On this basis, we first demonstrate the fast tracking ability of the perfect-sweep NLMS algorithm via computer generated simulation of a time-variant acoustic system. Then, the robustness of the perfect-sweep NLMS algorithm against non-linear characteristics of real measurements in a time-invariant case is presented. By finally addressing the measurement of quasi-continuous head-related impulse responses, we face the combined challenge of time-variant and possibly non-linear distorted acoustic system identification in a real application scenario and we can demonstrate the superiority of the perfect-sweep NLMS algorithm.