Evaluating Reverberation Models for Augmented Reality

Lee KY, Meyer-Kahlen N, Schlecht SJ, Välimäki V. Evaluating Reverberation Models for Augmented Reality. J Audio Eng Soc. 2025;73(10):619-632. doi:10.17743/jaes.2022.0223

@article{Lee2025_23025,
  author = {Lee, Kyung Yun and Meyer-Kahlen, Nils and Schlecht, Sebastian J. and Välimäki, Vesa},
  title = {{Evaluating Reverberation Models for Augmented Reality}},
  journal = {Journal of the Audio Engineering Society},
  volume = {73},
  number = {10},
  pages = {619--632},
  year = {2025},
  month = oct,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2022.0223},
  url = {https://doi.org/10.17743/jaes.2022.0223}
}

TY  - JOUR
TI  - Evaluating Reverberation Models for Augmented Reality
AU  - Lee, Kyung Yun
AU  - Meyer-Kahlen, Nils
AU  - Schlecht, Sebastian J.
AU  - Välimäki, Vesa
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 73
IS  - 10
SP  - 619
EP  - 632
PY  - 2025
DA  - 2025/10/01
DO  - 10.17743/jaes.2022.0223
UR  - https://doi.org/10.17743/jaes.2022.0223
PB  - Audio Engineering Society
LA  - en
AB  - The ultimate goal of the reverberation model for augmented reality is to create an auditory illusion, making simulated sound sources indistinguishable from real, measured ones. However, existing evaluation methods are not tailored to achieve this objective. This paper adopts the evaluation paradigm of auditory illusion tests to evaluate reverberation models under two distinct tasks: authenticity and transferring. The listening test uses the three-alternative forced-choice design, where subjects are asked to detect the speech signal processed with a model-generated room impulse response (RIR) among the signals processed with measured RIRs. For the authenticity task, the three signals contain the same speech sample, while for the transferring task, they contain different samples from different speakers. A Bayesian analysis shows that detecting model-generated RIRs is significantly more challenging in the transferring task than in the authenticity task across all models. Additionally, while the listening test results positively correlate with the selected objective metrics, the reliability and generalizability of these correlations for predicting listening test outcomes remain uncertain. The proposed evaluation framework for reverberation models can serve as a precursory analysis for developing dynamic, binaural rendering for augmented reality applications.
ER  -