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HRTF Upsampling and Personalization via Neural Operators — arXiv:2610.20400

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.20400
License (as archived): CC0-1.0
Archived: 2026-10-15T14:23:51.674Z
SHA-256 of the archived text: 92397f8026a7fb116e640fc3ce7c417c729d621494cf47ba73e2b9c88a6e00c5
Stable URL of this page: https://metraipolis.com/documento/dc_24e577104efc

How to cite

HRTF Upsampling and Personalization via Neural Operators — arXiv:2610.20400 — arXiv (en), https://arxiv.org/abs/2610.20400 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_24e577104efc (Metraipolis archive dc_24e577104efc, sha256 92397f8026a7fb116e640fc3ce7c417c729d621494cf47ba73e2b9c88a6e00c5)

BibTeX

@misc{dc24e577104efc,
  title = {HRTF Upsampling and Personalization via Neural Operators — arXiv:2610.20400},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.20400},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_24e577104efc; sha256 92397f8026a7fb116e640fc3ce7c417c729d621494cf47ba73e2b9c88a6e00c5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.20400. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Sound (cs.SD) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.20400 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T13:53:35.965Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).