| Paper ID | AUD-16.4 |
| Paper Title |
PERSONALIZED HRTF MODELING USING DNN-AUGMENTED BEM |
| Authors |
Mengfan Zhang, Stanford University, United States; Jui-Hsien Wang, Adobe Research, United States; Doug James, Stanford University, United States |
| 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 |
Accurate modeling of personalized head-related transfer functions (HRTFs) is difficult but critical for applications requiring spatial audio. However, this remains challenging as experimental measurements require specialized equipment, numerical simulations require accurate head geometries and robust solvers, and data-driven methods are hungry for data. In this paper, we propose a new deep learning method that combines measurements and numerical simulations to take the best of three worlds. By learning the residual difference and establishing a high quality spatial basis, our method achieves consistently 2 dB to 2.5 dB lower spectral distortion (SD) compared to the state-of-the-art methods. |
