Currently, the commercialization of high-quality virtual auditory display technology is limited by the costly and time-consuming methods required for obtaining listener-specific head-related transfer functions (HRTFs), directionally-dependent filters that encode spatial information. As such, there is an increased interest in the estimation of individualized HRTFs based on non-acoustic data. This study highlights the capabilities of an evolutionary algorithm method applied to the complex parameter optimization problem that arises when HRTFs are fit to individuals (or populations), rather than acoustically measured. Results suggest the algorithm may be capable of providing HRTFs that improve localization through both personalization of generic HRTFs and the generation of an optimized set of generic HRTFs.
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