# HRTF Upsampling and Personalization via Neural Operators

arXiv: https://arxiv.org/abs/2610.20400 · HTML: https://mlsanity.com/paper/2610.20400/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators](https://mlsanity.com/paper/1910.03193/) (2019)
- [Latent Neural Operator for Solving Forward and Inverse PDE Problems](https://mlsanity.com/paper/2406.03923/) (2024)
- [NIIRF: Neural IIR Filter Field for HRTF Upsampling and Personalization](https://mlsanity.com/paper/2402.17907/) (2024)
- [Magnitude-Corrected and Time-Aligned Interpolation of Head-Related Transfer Functions](https://mlsanity.com/paper/2303.09966/) (2023)
- [HRTF Field: Unifying Measured HRTF Magnitude Representation with Neural Fields](https://mlsanity.com/paper/2210.15196/) (2022)
- [Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization](https://mlsanity.com/paper/2501.13017/) (2025)
- [Graph Neural Field with Spatial-Correlation Augmentation for HRTF Personalization](https://mlsanity.com/paper/2511.10697/) (2025)
- [GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems](https://mlsanity.com/paper/2607.01128/) (2026)

## Abstract (arXiv)

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 interaural level difference (ILD) error at every sparsity level from 3 to 100 measurements (18% lower LSD and 43% lower ILD error than the strongest baseline at 100 measurements) while remaining competitive in interaural time difference (ITD) error. It reconstructs a dense HRTF 15 to 373 times faster than the leading neural-field method RANF. We further validate the approach on HUTUBS, demonstrating its effectiveness across HRTF datasets.