An approach for mesh-based generation of spatially dense, lowpass-filtered, individualized HRTFs using dynamic data acquisition

Heeb T, Leidi T, Vancheri A, Quattrini A, Grossi L, Spagnoli N, Oldano G. An approach for mesh-based generation of spatially dense, lowpass-filtered, individualized HRTFs using dynamic data acquisition. In: Convention Paper. Audio Engineering Society; 2024. Paper 10685. Available from: https://aes.org/publications/elibrary-page/?id=22498

@inproceedings{Heeb2024_22498,
  author = {Heeb, Thierry and Leidi, Tiziano and Vancheri, Alberto and Quattrini, Andrea and Grossi, Loris and Spagnoli, Noah and Oldano, Gilles},
  title = {{An approach for mesh-based generation of spatially dense, lowpass-filtered, individualized HRTFs using dynamic data acquisition}},
  note = {Paper 10685},
  year = {2024},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22498}
}

TY  - CPAPER
TI  - An approach for mesh-based generation of spatially dense, lowpass-filtered, individualized HRTFs using dynamic data acquisition
AU  - Heeb, Thierry
AU  - Leidi, Tiziano
AU  - Vancheri, Alberto
AU  - Quattrini, Andrea
AU  - Grossi, Loris
AU  - Spagnoli, Noah
AU  - Oldano, Gilles
M1  - Paper 10685
PY  - 2024
DA  - 2024/06/06
UR  - https://aes.org/publications/elibrary-page/?id=22498
PB  - Audio Engineering Society
LA  - en
AB  - Head Related Transfer Functions (HRTFs) capture the binaural information required for correct identification of a sound source position in 3D space. They are individual to each user and heavily depend on the direction of arrival of the sound from the considered source. Acquisition of personalized HRTFs for all possible directions is a lengthy process requiring carefully calibrated measurements which is difficult to apply in practical situations. In this paper we propose a data-driven, machine-learning based approach for the generation of personalized, direction-dense, lowpass-filtered HRTFs computed from a mesh of the users head and from a non-uniform set of dynamic data measurements. We present the different steps of our approach and show results from laboratory experiments. Comparison with a state-of-the-art, BEM-based HRTF generation method confirms the effectiveness of the proposed solution.
ER  -