A Magnitude-Based Parametric Model Predicting the Audibility of HRTF Variation

Doma S, Ermert CA, Fels J. A Magnitude-Based Parametric Model Predicting the Audibility of HRTF Variation. J Audio Eng Soc. 2023;71(4):155-172. doi:10.17743/jaes.2022.0080

@article{Doma2023_22038,
  author = {Doma, Shaimaa and Ermert, Cosima A. and Fels, Janina},
  title = {{A Magnitude-Based Parametric Model Predicting the Audibility of HRTF Variation}},
  journal = {Journal of the Audio Engineering Society},
  volume = {71},
  number = {4},
  pages = {155--172},
  year = {2023},
  month = apr,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2022.0080},
  url = {https://doi.org/10.17743/jaes.2022.0080}
}

TY  - JOUR
TI  - A Magnitude-Based Parametric Model Predicting the Audibility of HRTF Variation
AU  - Doma, Shaimaa
AU  - Ermert, Cosima A.
AU  - Fels, Janina
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 71
IS  - 4
SP  - 155
EP  - 172
PY  - 2023
DA  - 2023/04/06
DO  - 10.17743/jaes.2022.0080
UR  - https://doi.org/10.17743/jaes.2022.0080
PB  - Audio Engineering Society
LA  - en
AB  - This work proposes a parametric model for just noticeable differences of unilateral differences in head-related transfer functions (HRTFs). For seven generic magnitude-based distance metrics, common trends in their response to inter-individual and intra-individual HRTF differences are analyzed, identifying metric subgroups with pseudo-orthogonal behavior. On the basis of three representative metrics, a three-alternative forced-choice experiment is conducted, and the acquired discrimination probabilities are set in relation with distance metrics via different modeling approaches. A linear model, with coefficients based on principal component analysis and three distance metrics as input, yields the best performance, compared to a simple multi-linear regression approach or to principal component analysis--based models of higher complexity.
ER  -