HRTF Upsampling and Personalization via Neural Operators
Head-related transfer function (HRTF) spatial upsampling from sparse measurements is a fundamental challenge in personalized binaural audio. We formulate HRTF upsampling as an operator learning problem and introduce SPARO, a neural operator that maps sparse measurements at a set of spatial directions to the HRTF field, a continuous representation that can be queried at arbitrary positions. SPARO projects the measurements onto a learned set of spherical latent modes, processes the latent modes with Transformer blocks, and decodes the resulting representations at query directions. On SONICOM, using the LAP24 measurement configuration, SPARO attains the best log-spectral distortion (LSD) and...
| Authors | Meenakshi Krishnan, You Zhang, Ramani Duraiswami et al. |
|---|---|
| Primary category | cs.SD |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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