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 individualized HRTF sets. We present and assess a system for HRTF selection that relies on holistic judgments 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, randomized selection between equally judged HRTFs did not signi?cantly 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.

Authors: Shukla, Rishi; Stewart, Rebecca; Roginska, Agnieszka; Sandler, Mark
Affiliations: Queen Mary University London, London, UK; New York University, New York, NY, USA(See document for exact affiliation information.)
AES Conference: 2018 AES International Conference on Audio for Virtual and Augmented Reality (August 2018)
Paper Number: P4-2
Publication Date: August 11, 2018

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