| Paper ID | AUD-16.3 |
| Paper Title |
APPLIED METHODS FOR SPARSE SAMPLING OF HEAD-RELATED TRANSFER FUNCTIONS |
| Authors |
Lior Arbel, Ben-Gurion University of the Negev, Israel; Zamir Ben-Hur, David Lou Alon, Facebook Reality Labs Research, United States; Boaz Rafaely, Ben-Gurion University of the Negev, Israel |
| Session | AUD-16: Modeling, Analysis and Synthesis of Acoustic Environments 2: Spatial Audio |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Audio and Acoustic Signal Processing: [AUD-SARR] Spatial Audio Recording and Reproduction |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Production of high fidelity spatial audio applications requires individual head-related transfer functions (HRTFs). As the acquisition of HRTF is an elaborate process, interest lies in interpolating full length HRTF from sparse samples. Ear-alignment is a recently developed pre-processing technique, shown to reduce an HRTF’s spherical harmonics order, thus permitting sparse sampling over fewer directions. This paper describes the application of two methods for ear-aligned HRTF interpolation by sparse sampling: Orthogonal Matching Pursuit and Principal Component Analysis. These methods consist of generating unique vector sets for HRTF representation. The methods were tested over an HRTF dataset, indicating that interpolation errors using small sampling schemes may be further reduces by up to 5dB in comparison with spherical harmonics interpolation. |
