E. A. Durant and G. H. Wakefield, “Efficient model fitting using a genetic algorithm: Pole-zero approximations of HRTFs,” IEEE Trans. Speech Audio Processing, vol. 10, no. 1, pp. 18–27, 2002.

Abstract

We demonstrate that a genetic algorithm (GA) can efficiently generate accurate low-order pole-zero approximations of head-related transfer functions (HRTFs) from measured impulse responses by minimizing a logarithmic error criterion. This approach is much simpler and comparable or superior in efficiency to competing search algorithms. We build on previous work in low-order HRTF approximation [1]. By applying the GA, we converge to solutions of equal quality in about 30 s compared to over 20 min.

This work develops a basic steady-state GA using a pole-zero filter design problem as an illustrative example. We propose a domain-appropriate error measure. We then apply the algorithm to designing filters to approximate measured HRTFs. Detailed performance measurements are presented. In the Appendix, we propose a widely applicable population variation metric. A lower bound is developed for this metric and is used to detect convergence.

[1] M. A. Blommer and G. H. Wakefield, “Pole-zero approximations for head-related transfer functions using a logarithmic error criterion,” IEEE Trans. Speech Audio Processing, vol. 5, pp. 278–287, May 1997.