Quantifying HRTF Spectral Magnitude Precision in Spatial Computing Applications

Crawford S, Audfray R, Jot JM. Quantifying HRTF Spectral Magnitude Precision in Spatial Computing Applications. In: AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020). Audio Engineering Society; 2020. Paper 1-6. Available from: https://aes.org/publications/elibrary-page/?id=20869

@inproceedings{Crawford2020_20869,
  author = {Crawford, Steven and Audfray, Rémi and Jot, Jean-Marc},
  title = {{Quantifying HRTF Spectral Magnitude Precision in Spatial Computing Applications}},
  booktitle = {AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)},
  note = {Paper 1-6},
  year = {2020},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20869}
}

TY  - CPAPER
TI  - Quantifying HRTF Spectral Magnitude Precision in Spatial Computing Applications
AU  - Crawford, Steven
AU  - Audfray, Rémi
AU  - Jot, Jean-Marc
T2  - AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)
M1  - Paper 1-6
PY  - 2020
DA  - 2020/08/06
UR  - https://aes.org/publications/elibrary-page/?id=20869
PB  - Audio Engineering Society
LA  - en
AB  - In this paper, an algorithmic approach towards computing quantifiable metrics regarding HRTF spectral magnitude synthesis performance of virtual sound systems, such as those used in VR/AR/MR environments, is presented. Utilizing regularized regression in parallel with a statistical information theory technique, the system provides a detailed analysis of a virtual spatializer’s spectral magnitude rendering accuracy at a given point in space. Applying the proposed system to the final signal processing stage of a spatial audio rendering pipeline enables the engineer to establish critical performance quantities for benchmarking future modifications to the rendering channel against. The proposed system demonstrates an important step towards standardizing and automating virtual audio system evaluation and may ultimately act as a participant substitute during critical listening tasks.
ER  -