Head-Related Transfer Functions Reconstruction from Sparse Measurements Considering a Priori Knowledge from Database Analysis: A Pattern Recognition Approach

Guillon P, Nicol R, Simon L. Head-Related Transfer Functions Reconstruction from Sparse Measurements Considering a Priori Knowledge from Database Analysis: A Pattern Recognition Approach. In: AES Convention 125. Audio Engineering Society; 2008. Paper 7610. Available from: https://aes.org/publications/elibrary-page/?id=14761

@inproceedings{Guillon2008_14761,
  author = {Guillon, Pierre and Nicol, Rozenn and Simon, Laurent},
  title = {{Head-Related Transfer Functions Reconstruction from Sparse Measurements Considering a Priori Knowledge from Database Analysis: A Pattern Recognition Approach}},
  booktitle = {AES Convention 125},
  note = {Paper 7610},
  year = {2008},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=14761}
}

TY  - CPAPER
TI  - Head-Related Transfer Functions Reconstruction from Sparse Measurements Considering a Priori Knowledge from Database Analysis: A Pattern Recognition Approach
AU  - Guillon, Pierre
AU  - Nicol, Rozenn
AU  - Simon, Laurent
T2  - AES Convention 125
M1  - Paper 7610
PY  - 2008
DA  - 2008/10/06
UR  - https://aes.org/publications/elibrary-page/?id=14761
PB  - Audio Engineering Society
LA  - en
AB  - Individualized Head-Related Transfer Functions (HRTFs) are required to achieve high quality Virtual Auditory Spaces. This study proposes to decrease the total number of measured directions in order to make acoustic measurements more comfortable. To overcome the limit of sparseness for which classical interpolation techniques fail to properly reconstruct HRTFs, additional knowledge has to be injected. Focusing on the spatial structure of HRTFs, the analysis of a large HRTF database enables to introduce spatial prototypes. After a pattern recognition process, these prototypes serve as a well-informed background for the reconstruction of any sparsely measured set of individual HRTFs. This technique shows better spatial fidelity than blind interpolation techniques.
ER  -