Analysis and optimal control of LMS-type adaptive filtering for continuous-azimuth acquisition of head related impulse responses

Abstract

Head related impulse responses (HRIRs) are the key to spatial realism in auditory virtual environments (AVEs). However, the measurement of discrete-azimuth HRIRs and their interpolation has been recognized as a tedious and delicate experimental procedure. We therefore suggest an adaptive filtering concept for continuous HRIR acquisition that completely avoids the traditional sampling and interpolation issue. Using an LMS-type adaptive algorithm, the HRIRs - at any azimuth - are extracted from a one-shot binaural recording. During data acquisition, the subject of interest is continuously rotated in the horizontal plane in order to capture the corresponding spatial information. In particular, the paper provides a profound theoretical and experimental analysis of the resulting HRIR inaccuracy in terms of the mean-square error. Furthermore, the optimal step- size parameter of the LMS-type adaptive algorithm is determined for which the minimum HRIR inaccuracy is attained.