User selection of optimal HRTF sets via holistic comparative evaluation

If well-matched to a given listener, head-related transfer functions (HRTFs) that have not been individually measured can still present relatively effective auditory scenes compared to renderings from individualised HRTF sets. We present and assess a system for HRTF selection that relies on holistic judgements of users to identify their optimal match through a series of pairwise adversarial comparisons. The mechanism resulted in clear preference for a single HRTF set in a majority of cases. Where this did not occur, randomised selection between equally judged HRTFs did not significantly impact user performance in a subsequent listening task. This approach is shown to be equally effective for both novice and expert listeners in selecting their preferred HRTF set.