Perceptual Comparison of Parametric Spatial Audio Codings for Binaural Rendering

Stahl B, Riedel S. Perceptual Comparison of Parametric Spatial Audio Codings for Binaural Rendering. In: AES Conference: AES 2023 International Conference on Spatial and Immersive Audio. Audio Engineering Society; 2023. Paper 13. Available from: https://aes.org/publications/elibrary-page/?id=22205

@inproceedings{Stahl2023_22205,
  author = {Stahl, Benjamin and Riedel, Stefan},
  title = {{Perceptual Comparison of Parametric Spatial Audio Codings for Binaural Rendering}},
  booktitle = {AES Conference: AES 2023 International Conference on Spatial and Immersive Audio},
  note = {Paper 13},
  year = {2023},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22205}
}

TY  - CPAPER
TI  - Perceptual Comparison of Parametric Spatial Audio Codings for Binaural Rendering
AU  - Stahl, Benjamin
AU  - Riedel, Stefan
T2  - AES Conference: AES 2023 International Conference on Spatial and Immersive Audio
M1  - Paper 13
PY  - 2023
DA  - 2023/08/06
UR  - https://aes.org/publications/elibrary-page/?id=22205
PB  - Audio Engineering Society
LA  - en
AB  - Parametric coding of sound fields into directional and diffuse components is a common approach to spatial up-mixing of low-order Ambisonic signals and impulse responses. In this contribution we investigate parametric representations of sound fields considering one or two directional components per time-frequency bin. Additionally, a purely isotropic diffuse stream or a first-order directional ambient stream is considered to account for residual spatial components. A binaural listening experiment was conducted, comparing third-order parametrically synthe-sized scenes to a third-order Ambisonic reference. The improvement gained by a second directional component per time-frequency bin is mainly observable under anechoic conditions. The variants with a first-order directional ambient stream better account for residual spatial components in cases where not all direct sound sources can be represented by directional components and under reverberant conditions. This makes them well-suited as baselines in further studies on parametric coding and upmixing of sound fields and potentially useful as training targets for deep neural network approaches in parametric coding and upmixing.
ER  -