The role of spatial detail in sound-source localization: Impact on HRTF modeling and personalization.

While current head-related transfer function (HRTF) personalization methods offer some ability to quickly customize spatial auditory displays, these techniques generally lack the realism and performance provided by full individualized HRTF measurements. This poor performance is likely due to the vast amount of individual spectral and spatial variation contained in a measured HRTF. While some of this variation contains important directional information, Kulkarni and Colburn (1998) showed that perceptually irrelevant spectral variation could be eliminated by smoothing the HRTF magnitude with a truncated Fourier series expansion. The present study investigates a related method for smoothing the spatial variation contained in an HRTF magnitude by utilizing a truncated spherical harmonic expansion. The perceptual impacts of various degrees of spatial smoothing were evaluated by comparing performance to performance obtained with full individualized HRTF measurements in a virtual localization task. Results indicate that comparable localization performance can be achieved with as low as a fourth-order spherical harmonic representation, which provides a significant amount of spatial smoothing. Analysis of the resulting simplified representation also uncovered a number of interesting relationships across individuals that may facilitate the development of future techniques that estimate and personalize HRTFs.