Efficient State-Space Interpolation of HRTFs
The paper addresses the task of spatially interpolating the head related transfer function (HRTF) for correct updating of binaural 3D sound synthesis in response to head-movements in telepresence environments. Using HRTF data, we create proper matrix transfer functions or equivalently appropriate state-space realizations. We base our method on the factorization of a block Loewner matrix into a product of generalized observability and controllability matrices. We recollect certain properties to be satisfied by the Loewner matrix, and use it to construct a minimal state-space realization of an interpolating matrix transfer function. Compared to existing interpolation methods, this novel approach proved to be outperforming and capable of insuring high-fidelity reconstructions of HRTFs.
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!
This paper costs $33 for non-members and is temporarily free for AES members.