Perceptual comparison of efficient real-time geometrical acoustics engines in Virtual Reality

Amengual Gari SV, Schissler C, Robinson P. Perceptual comparison of efficient real-time geometrical acoustics engines in Virtual Reality. In: AES Conference: AES 2024 International Audio for Games Conference. Audio Engineering Society; 2024. Paper 6. Available from: https://aes.org/publications/elibrary-page/?id=22413

@inproceedings{AmengualGari2024_22413,
  author = {Amengual Gari, Sebastia Vicenc and Schissler, Carl and Robinson, Philip},
  title = {{Perceptual comparison of efficient real-time geometrical acoustics engines in Virtual Reality}},
  booktitle = {AES Conference: AES 2024 International Audio for Games Conference},
  note = {Paper 6},
  year = {2024},
  month = apr,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22413}
}

TY  - CPAPER
TI  - Perceptual comparison of efficient real-time geometrical acoustics engines in Virtual Reality
AU  - Amengual Gari, Sebastia Vicenc
AU  - Schissler, Carl
AU  - Robinson, Philip
T2  - AES Conference: AES 2024 International Audio for Games Conference
M1  - Paper 6
PY  - 2024
DA  - 2024/04/06
UR  - https://aes.org/publications/elibrary-page/?id=22413
PB  - Audio Engineering Society
LA  - en
AB  - Interactive immersive experiences and games require the dynamic modelling of acoustical phenomena over large and complex geometrical environments. However, the emergence of mobile Virtual Reality (VR) platforms and the ever limited computational budget for audio processing imposes severe constraints on the simulation process. With this in mind, efficient geometrical acoustics (GA) real-time engines are an attractive alternative. In this work we present the results of a perceptual comparison between three geometrical acoustic engines suitable for VR environments: an engine based on an Image Source Model (ISM) of a shoebox of variable dimensions, a path tracing (PT) engine with arbitrary geometry and frequency dependent materials, and a bi-directional path tracing (BDPT) engine with perceptual optimization of the Head-Related Transfer Function. The tests were conducted using Meta Quest and Quest 2 headsets and 26 listeners provided perceptual ratings of six attributes (preference, realism/naturalness, reverb quality, localization, distance, spatial impression) of three different sources in 6 scenes. The results reveal that the BDPT engine is consistently rated higher than the other two in 4 of the perceptual attributes i.e. preference, realism/naturalness, reverberation quality, and spatial impression, particularly in large reverberant spaces. In small spaces, trends are less clear and ratings are more subject dependent. A Principal Component Analysis (PCA) revealed that only two perceptual dimensions account for more than 80% of the explained variance of the ratings.
ER  -