Individualized HRTF-based Binaural Renderer for Higher-Order Ambisonics

Zhang M, Guan T, Chen L, Fu T, Su D, Qu T. Individualized HRTF-based Binaural Renderer for Higher-Order Ambisonics. In: AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020). Audio Engineering Society; 2020. Paper 10454. Available from: https://aes.org/publications/elibrary-page/?id=21131

@inproceedings{Zhang2020_21131,
  author = {Zhang, Mengfan and Guan, Tianyi and Chen, Lianwu and Fu, Tianxiao and Su, Dan and Qu, Tianshu},
  title = {{Individualized HRTF-based Binaural Renderer for Higher-Order Ambisonics}},
  booktitle = {AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)},
  note = {Paper 10454},
  year = {2020},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=21131}
}

TY  - CPAPER
TI  - Individualized HRTF-based Binaural Renderer for Higher-Order Ambisonics
AU  - Zhang, Mengfan
AU  - Guan, Tianyi
AU  - Chen, Lianwu
AU  - Fu, Tianxiao
AU  - Su, Dan
AU  - Qu, Tianshu
T2  - AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)
M1  - Paper 10454
PY  - 2020
DA  - 2020/08/06
UR  - https://aes.org/publications/elibrary-page/?id=21131
PB  - Audio Engineering Society
LA  - en
AB  - Ambisonics is a promising spatial sound technique in augmented and virtual reality. In our previous study, we modeled the individual head-related transfer functions (HRTFs) using deep neural networks based on spatial principal component analysis. This paper proposes an individualized HRTF-based binaural renderer for the higher-order Ambisonics. The binaural renderer is implemented by filtering the virtual loudspeaker signals using individualized HRTFs. We perform subjective experiments to evaluate generic and individualized binaural renderers. Results show that the individualized binaural renderer has front-back confusion rates that are significantly lower than those of the generic binaural renderer. Therefore, we validate that using individualized HRTFs to convolve with those virtual loudspeaker signals to generate virtual sound at an arbitrary spatial direction still performs better than those using generic HRTFs. In addition, by measuring or modeling individual’s HRTFs in a small set of directions, our proposed binaural renderer system effectively predict individual’s HRTFs in arbitrary spatial directions.
ER  -