Predicting Perceptual Transparency of Head-Worn Devices

Lladó P, Mckenzie T, Meyer-Kahlen N, Schlecht SJ. Predicting Perceptual Transparency of Head-Worn Devices. J Audio Eng Soc. 2022;70(7/8):585-600. doi:10.17743/jaes.2022.0024

@article{Llado2022_21825,
  author = {Lladó, Pedro and Mckenzie, Thomas and Meyer-Kahlen, Nils and Schlecht, Sebastian J.},
  title = {{Predicting Perceptual Transparency of Head-Worn Devices}},
  journal = {Journal of the Audio Engineering Society},
  volume = {70},
  number = {7/8},
  pages = {585--600},
  year = {2022},
  month = jul,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2022.0024},
  url = {https://doi.org/10.17743/jaes.2022.0024}
}

TY  - JOUR
TI  - Predicting Perceptual Transparency of Head-Worn Devices
AU  - Lladó, Pedro
AU  - Mckenzie, Thomas
AU  - Meyer-Kahlen, Nils
AU  - Schlecht, Sebastian J.
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 70
IS  - 7/8
SP  - 585
EP  - 600
PY  - 2022
DA  - 2022/07/06
DO  - 10.17743/jaes.2022.0024
UR  - https://doi.org/10.17743/jaes.2022.0024
PB  - Audio Engineering Society
LA  - en
AB  - Acoustically transparent head-worn devices are a key component of auditory augmented reality systems, in which both real and virtual sound sources are presented to a listener simultaneously. Head-worn devices can exhibit high transparency simply through their physical design but in practice will always obstruct the sound field to some extent. In this study, a method for predicting the perceptual transparency of head-worn devices is presented using numerical analysis of device measurements, testing both coloration and localization in the horizontal and median plane. Firstly, listening experiments are conducted to assess perceived coloration and localization impairments. Secondly, head-related transfer functions of a dummy head wearing the head-worn devices are measured, and auditory models are used to numerically quantify the introduced perceptual effects. The results show that the tested auditory models are capable of predicting perceptual transparency and are therefore robust in applications that they were not initially designed for.
ER  -