# NIIRF: Neural IIR Filter Field for HRTF Upsampling and Personalization

arXiv: https://arxiv.org/abs/2402.17907 · HTML: https://mlsanity.com/paper/2402.17907/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2024.

## Built on by (most cited first, 6 of 6)

- [Towards HRTF Personalization using Denoising Diffusion Models](https://mlsanity.com/paper/2501.02871/) (2025)
- [TTMBA: Towards Text To Multiple Sources Binaural Audio Generation](https://mlsanity.com/paper/2507.16564/) (2025)
- [Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening](https://mlsanity.com/paper/2509.02571/) (2025)
- [HRTFformer: A Spatially-Aware Transformer for Individual HRTF Upsampling in Immersive Audio Rendering](https://mlsanity.com/paper/2510.01891/) (2025)
- [HRIR-Former: Grid-Free Time-Domain Reconstruction of Head-Related Impulse Responses with a Spatially Encoded Transformer](https://mlsanity.com/paper/2603.27998/) (2026)
- [HRTF Upsampling and Personalization via Neural Operators](https://mlsanity.com/paper/2610.20400/) (2026)

## Abstract (arXiv)

Head-related transfer functions (HRTFs) are important for immersive audio, and their spatial interpolation has been studied to upsample finite measurements. Recently, neural fields (NFs) which map from sound source direction to HRTF have gained attention. Existing NF-based methods focused on estimating the magnitude of the HRTF from a given sound source direction, and the magnitude is converted to a finite impulse response (FIR) filter. We propose the neural infinite impulse response filter field (NIIRF) method that instead estimates the coefficients of cascaded IIR filters. IIR filters mimic the modal nature of HRTFs, thus needing fewer coefficients to approximate them well compared to FIR filters. We find that our method can match the performance of existing NF-based methods on multiple datasets, even outperforming them when measurements are sparse. We also explore approaches to personalize the NF to a subject and experimentally find low-rank adaptation to be effective.