On the Improvement of Localization Accuracy with Non-Individualized HRTF-Based Sounds
Even though individual head-related transfer function (HRTF) filters produce better performance in virtual-reality environments, measuring individuals is labor intensive and expensive. Can training be used to enhance the performance of generic filters? This research shows that short training sessions with feedback allows for perceptual adaptation where simple exposure to generic HRTF filters did not. The benefits of training were observed not only for the trained sounds but also for other stimulus positions that were not part of the training. Apparently, subjects were actually adapting and generalizing to the generic HRTF filters, which is a manifestation of sensory neural plasticity. Learning profiles are unique to individuals. Any testing of localization performance should recognize the influence of training.
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