Predicting the Perceptibility of Room Acoustic Variations Using Generalized Linear Mixed Models

Treybig L, Klein F, Werner S, Amengual Garí SV, Göring S. Predicting the Perceptibility of Room Acoustic Variations Using Generalized Linear Mixed Models. J Audio Eng Soc. 2025;73(11):692-706. doi:10.17743/jaes.2022.0232

@article{Treybig2025_23102,
  author = {Treybig, Lukas and Klein, Florian and Werner, Stephan and Amengual Garí, Sebastià V. and Göring, Steve},
  title = {{Predicting the Perceptibility of Room Acoustic Variations Using Generalized Linear Mixed Models}},
  journal = {Journal of the Audio Engineering Society},
  volume = {73},
  number = {11},
  pages = {692--706},
  year = {2025},
  month = nov,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2022.0232},
  url = {https://doi.org/10.17743/jaes.2022.0232}
}

TY  - JOUR
TI  - Predicting the Perceptibility of Room Acoustic Variations Using Generalized Linear Mixed Models
AU  - Treybig, Lukas
AU  - Klein, Florian
AU  - Werner, Stephan
AU  - Amengual Garí, Sebastià V.
AU  - Göring, Steve
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 73
IS  - 11
SP  - 692
EP  - 706
PY  - 2025
DA  - 2025/11/10
DO  - 10.17743/jaes.2022.0232
UR  - https://doi.org/10.17743/jaes.2022.0232
PB  - Audio Engineering Society
LA  - en
AB  - Acoustic augmented realities aim to incorporate virtual elements on top of real acoustic environments. A key question is how accurately virtual acoustics must match real acoustics to enable perceptually seamless integration. In six-degrees-of-freedom scenarios, where listeners can explore spaces, intraroom acoustic variation is of particular interest. This paper presents a method for modeling the perceptibility of acoustic changes in an acoustic augmented realities context using a generalized linear mixed model based on differences in room acoustic parameters computed from pairs of room impulse responses. An online listening test based on extensive acoustic measurements investigated perceptible differences between three acoustic configurations of the same room using identical source-receiver arrangements. A total of 4,753 ratings from 120 participants in an ABX-like format (a method where participants identify whether a presented stimulus X matches stimulus A or B) were used to train the generalized linear mixed model. The resulting model shows good predictive performance (Brier score: 0.19; AUC: 0.67) based on a fivefold cross-validation with 100 repetitions, although improvements are possible. However, the perceptibility of acoustic changes can vary significantly between dynamic and static listening scenarios. The paper discusses evaluation strategies for these aspects, examines the model’s generalizability, highlights current limitations, and outlines directions for future improvement.
ER  -