Optimal approximation of head-related transfer function’s pole-zero model based on genetic algorithm

Abstract

In the research on spatial hearing and virtual auditory space, it is important to effectively model the head-related transfer functions (HRTFs). Based on the analysis of the HRTFs  spectrum and some perspectives of psychoacoustics, this paper applied multiple demes  parallel and real-valued coding genetic algorithm (GA) to approximate the HRTFs  zero-pole model. Using the logarithmic magnitude s error criterion for the human auditory sense, the results show that the performance of the GA is on the average 39 % better than that of the traditional Prony method, and 46 % better than that of the Yule-Walker algorithm.