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S. Doma, CO. A.. Ermert, and J. Fels, "A Magnitude-Based Parametric Model Predicting the Audibility of HRTF Variation," J. Audio Eng. Soc., vol. 71, no. 4, pp. 155-172, (2023 April.). doi: https://doi.org/10.17743/jaes.2022.0080 S. Doma, CO. A.. Ermert, and J. Fels, "A Magnitude-Based Parametric Model Predicting the Audibility of HRTF Variation," J. Audio Eng. Soc., vol. 71 Issue 4 pp. 155-172, (2023 April.). doi: https://doi.org/10.17743/jaes.2022.0080
Abstract: 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.

@article{doma2023a,
author={doma, shaimaa and ermert, cosima a. and fels, janina},
journal={journal of the audio engineering society},
title={a magnitude-based parametric model predicting the audibility of hrtf variation},
year={2023},
volume={71},
number={4},
pages={155-172},
doi={https://doi.org/10.17743/jaes.2022.0080},
month={april},} @article{doma2023a,
author={doma, shaimaa and ermert, cosima a. and fels, janina},
journal={journal of the audio engineering society},
title={a magnitude-based parametric model predicting the audibility of hrtf variation},
year={2023},
volume={71},
number={4},
pages={155-172},
doi={https://doi.org/10.17743/jaes.2022.0080},
month={april},
abstract={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.},}

TY - paper
TI - A Magnitude-Based Parametric Model Predicting the Audibility of HRTF Variation
SP - 155 EP - 172
AU - Doma, Shaimaa
AU - Ermert, Cosima A.
AU - Fels, Janina
PY - 2023
JO - Journal of the Audio Engineering Society
IS - 4
VO - 71
VL - 71
Y1 - April 2023 TY - paper
TI - A Magnitude-Based Parametric Model Predicting the Audibility of HRTF Variation
SP - 155 EP - 172
AU - Doma, Shaimaa
AU - Ermert, Cosima A.
AU - Fels, Janina
PY - 2023
JO - Journal of the Audio Engineering Society
IS - 4
VO - 71
VL - 71
Y1 - April 2023
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.