@inproceedings{fa4e6ee75c974ab7a30059ae5f58777b,
title = "User selection of optimal HRTF sets via holistic comparative evaluation",
abstract = "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.",
author = "Rishi Shukla and Rebecca Stewart and Agnieszka Roginska and Mark Sandler",
note = "Publisher Copyright: {\textcopyright} Audio Engineering Society. All rights reserved.; AES International Conference on Audio for Virtual and Augmented Reality: Science, Technology, Design, and Implementation, AVAR 2018 ; Conference date: 20-08-2018 Through 22-08-2018",
year = "2018",
language = "English (US)",
series = "Proceedings of the AES International Conference",
publisher = "Audio Engineering Society",
pages = "155--164",
booktitle = "AES International Conference on Audio for Virtual and Augmented Reality 2018",
address = "United States",
}