A Perceptual Evaluation Method for Binaural Rendering Algorithms via Minimum Audible Angle Measurements

Qian Y, Zhu H, Wu X, Qu T. A Perceptual Evaluation Method for Binaural Rendering Algorithms via Minimum Audible Angle Measurements. In: Convention Paper. Audio Engineering Society; 2026.pp. 10292:1-9. Available from: https://aes.org/publications/elibrary-page/?id=23189

@inproceedings{Qian2026_23189,
  author = {Qian, Yufan and Zhu, Houlin and Wu, Xihong and Qu, Tianshu},
  title = {{A Perceptual Evaluation Method for Binaural Rendering Algorithms via Minimum Audible Angle Measurements}},
  pages = {10292:1-9},
  url = {https://aes.org/publications/elibrary-page/?id=23189},
  year = {2026},
  month = may,
  publisher = {Audio Engineering Society}
}

TY  - CPAPER
TI  - A Perceptual Evaluation Method for Binaural Rendering Algorithms via Minimum Audible Angle Measurements
AU  - Qian, Yufan
AU  - Zhu, Houlin
AU  - Wu, Xihong
AU  - Qu, Tianshu
SP  - 1
EP  - 9
M1  - Paper 10292
PY  - 2026
DA  - 2026/05/28
UR  - https://aes.org/publications/elibrary-page/?id=23189
PB  - Audio Engineering Society
LA  - en
AB  - Binaural rendering is typically assessed via timbre and localization accuracy, while its intrinsic spatial resolution remains rarely quantified. This paper proposes a perceptual evaluation method based on Minimum Audible Angle (MAA) measurements to estimate the azimuthal just-noticeable difference (JND) introduced by binaural rendering algorithms. We systematically compared several rendering algorithms across eight reference azimuths using two participant-allocation paradigms. The results show that spatial resolution is significantly influenced by Ambisonic order and choice of the rendering algorithm, with MAA thresholds systematically decreasing as the truncation order increases. Furthermore, the proposed method successfully captures physiological spatial characteristics and identifies resolution limits imposed by reference angles. While both participant-allocation paradigms yield consistent qualitative trends, the repeated-measures design provides superior data stability. These findings demonstrate that the proposed MAA-based method is an effective tool for quantifying the spatial resolution of binaural rendering algorithms.
ER  -