Perfect sequence lms for rapid acquisition of continuous-azimuth head related impulse responses

In recent publications, continuous-azimuth inference of head related impulse responses (HRIRs) was treated as a time-varying system identification problem on the basis of dynamical measurements. The system identification thus can be handled by LMS-type adaptive filters for which we have the freedom to choose the excitation signal in this application. In order to provide the perspective of reducing the measurement time to a minimum, we now suggest the optimal excitation signal in terms of the rate of convergence. This excitation signal is given by perfect sequences (PSEQs) out of the larger family of periodic pseudo-noise signals. After the discussion of specific implications of perfect sequences, we compare the performances of our perfect-sequence LMS algorithm (PSEQ-LMS) to the results of white noise processing. We demonstrate a uniform improvement by PSEQ-LMS in terms of instrumental mean-square error analysis as well as subjective listening to dynamic HRIRs. Both measures turn out to be consistent.