Interaural Level Difference Optimisation of First-Order Binaural Ambisonic Rendering

McKenzie T, Murphy D, Kearney G. Interaural Level Difference Optimisation of First-Order Binaural Ambisonic Rendering. In: AES Conference: 2019 AES International Conference on Immersive and Interactive Audio. Audio Engineering Society; 2019. Paper 17. Available from: https://aes.org/publications/elibrary-page/?id=20421

@inproceedings{McKenzie2019_20421,
  author = {McKenzie, Thomas and Murphy, Damian and Kearney, Gavin},
  title = {{Interaural Level Difference Optimisation of First-Order Binaural Ambisonic Rendering}},
  booktitle = {AES Conference: 2019 AES International Conference on Immersive and Interactive Audio},
  note = {Paper 17},
  year = {2019},
  month = mar,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20421}
}

TY  - CPAPER
TI  - Interaural Level Difference Optimisation of First-Order Binaural Ambisonic Rendering
AU  - McKenzie, Thomas
AU  - Murphy, Damian
AU  - Kearney, Gavin
T2  - AES Conference: 2019 AES International Conference on Immersive and Interactive Audio
M1  - Paper 17
PY  - 2019
DA  - 2019/03/06
UR  - https://aes.org/publications/elibrary-page/?id=20421
PB  - Audio Engineering Society
LA  - en
AB  - Binaural Ambisonic rendering is widely used in immersive applications such as virtual reality due to its sound field rotation capabilities. An issue of low-order Ambisonics is that interaural level differences (ILDs), a crucial cue for horizontal localisation, are often reproduced lower than they should be, which reduces lateralisation. This paper introduces a simple method for Ambisonic ILD Optimisation (AIO), aiming to bring the ILDs produced by binaural Ambisonic rendering closer to those of head-related impulse responses (HRIRs). AIO is evaluated using first-order Ambisonics versus a reference dataset of HRIRs for all locations on the sphere using estimated ILD, perceptual spectral difference and horizontal plane localisation. Results show an overall improvement in all tested metrics.
ER  -