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P. Guillon, T. Guignard, and R. Nicol, "Head-Related Transfer Function Customization by Frequency Scaling and Rotation Shift Based on a New Morphological Matching Method," Paper 7550, (2008 October.). doi: P. Guillon, T. Guignard, and R. Nicol, "Head-Related Transfer Function Customization by Frequency Scaling and Rotation Shift Based on a New Morphological Matching Method," Paper 7550, (2008 October.). doi:
Abstract: Head-Related Transfer Functions (HRTFs) individualization is required to achieve high quality Virtual Auditory Spaces. An alternative to acoustic measurements is the customization of non-individual HRTFs. To transform HRTF data, we propose a combination of frequency scaling and rotation shift, whose parameters are predicted by a new morphological matching method. For six subjects, mesh models of head and pinnae are acquired, and differences in size and orientation of the pinnae are evaluated with a modified Iterative Closest Point (ICP) algorithm. Optimal HRTF transformations are computed in parallel. A relatively good correlation between morphological and transformation parameters is found and allows to predict the customization parameters from the registration of pinna shapes. The resulting model achieves better customization than frequency scaling only, which shows that adding the rotation degree of freedom improves HRTF individualization.

@article{guillon2008head-related,
author={guillon, pierre and guignard, thomas and nicol, rozenn},
journal={journal of the audio engineering society},
title={head-related transfer function customization by frequency scaling and rotation shift based on a new morphological matching method},
year={2008},
volume={},
number={},
pages={},
doi={},
month={october},} @article{guillon2008head-related,
author={guillon, pierre and guignard, thomas and nicol, rozenn},
journal={journal of the audio engineering society},
title={head-related transfer function customization by frequency scaling and rotation shift based on a new morphological matching method},
year={2008},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={head-related transfer functions (hrtfs) individualization is required to achieve high quality virtual auditory spaces. an alternative to acoustic measurements is the customization of non-individual hrtfs. to transform hrtf data, we propose a combination of frequency scaling and rotation shift, whose parameters are predicted by a new morphological matching method. for six subjects, mesh models of head and pinnae are acquired, and differences in size and orientation of the pinnae are evaluated with a modified iterative closest point (icp) algorithm. optimal hrtf transformations are computed in parallel. a relatively good correlation between morphological and transformation parameters is found and allows to predict the customization parameters from the registration of pinna shapes. the resulting model achieves better customization than frequency scaling only, which shows that adding the rotation degree of freedom improves hrtf individualization.},}

TY - paper
TI - Head-Related Transfer Function Customization by Frequency Scaling and Rotation Shift Based on a New Morphological Matching Method
SP - EP -
AU - Guillon, Pierre
AU - Guignard, Thomas
AU - Nicol, Rozenn
PY - 2008
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2008 TY - paper
TI - Head-Related Transfer Function Customization by Frequency Scaling and Rotation Shift Based on a New Morphological Matching Method
SP - EP -
AU - Guillon, Pierre
AU - Guignard, Thomas
AU - Nicol, Rozenn
PY - 2008
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2008
AB - Head-Related Transfer Functions (HRTFs) individualization is required to achieve high quality Virtual Auditory Spaces. An alternative to acoustic measurements is the customization of non-individual HRTFs. To transform HRTF data, we propose a combination of frequency scaling and rotation shift, whose parameters are predicted by a new morphological matching method. For six subjects, mesh models of head and pinnae are acquired, and differences in size and orientation of the pinnae are evaluated with a modified Iterative Closest Point (ICP) algorithm. Optimal HRTF transformations are computed in parallel. A relatively good correlation between morphological and transformation parameters is found and allows to predict the customization parameters from the registration of pinna shapes. The resulting model achieves better customization than frequency scaling only, which shows that adding the rotation degree of freedom improves HRTF individualization.