Efficient and Accurate Multi-Source HRTF Rendering via Multi-Layer Optimization

Marchan M, Allen A. Efficient and Accurate Multi-Source HRTF Rendering via Multi-Layer Optimization. In: AES Conference: AES 2022 International Audio for Virtual and Augmented Reality Conference. Audio Engineering Society; 2022. Paper 9. Available from: https://aes.org/publications/elibrary-page/?id=21839

@inproceedings{Marchan2022_21839,
  author = {Marchan, Mick and Allen, Andrew},
  title = {{Efficient and Accurate Multi-Source HRTF Rendering via Multi-Layer Optimization}},
  booktitle = {AES Conference: AES 2022 International Audio for Virtual and Augmented Reality Conference},
  note = {Paper 9},
  year = {2022},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=21839}
}

TY  - CPAPER
TI  - Efficient and Accurate Multi-Source HRTF Rendering via Multi-Layer Optimization
AU  - Marchan, Mick
AU  - Allen, Andrew
T2  - AES Conference: AES 2022 International Audio for Virtual and Augmented Reality Conference
M1  - Paper 9
PY  - 2022
DA  - 2022/08/06
UR  - https://aes.org/publications/elibrary-page/?id=21839
PB  - Audio Engineering Society
LA  - en
AB  - Recent developments in AR / VR applications have brought a renewed focus on efficient and scalable real-time HRTF renderers to alleviate compute constraints when spatializing many sound sources at once. To efficiently achieve a reasonable approximation of the full-sphere, the HRTF dataset is often linearly decomposed into a predetermined number of basis filters via methods such as Ambisonics, VBAP, or PCA. This paper proposes a novel HRTF renderer and decomposition technique that, when compared to previous methods, allows for greater accuracy of the HRTF approximation for an equivalent compute cost. This is achieved through a multi-layered optimization network architecture that minimizes a perceptually motivated error function to derive the basis filters. We will demonstrate the numerical accuracy of our technique as well as provide listening test results comparing our method to other linear decomposition methods of relative computational cost using both our internal and the publicly available SADIE HRTF datasets.
ER  -