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