# HRTFformer: A Spatially-Aware Transformer for Individual HRTF Upsampling in Immersive Audio Rendering

arXiv: https://arxiv.org/abs/2510.01891 · HTML: https://mlsanity.com/paper/2510.01891/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 94 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Deep Back-Projection Networks For Super-Resolution](https://mlsanity.com/paper/1803.02735/) (2018)
- [Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding](https://mlsanity.com/paper/2103.15358/) (2021)
- [TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up](https://mlsanity.com/paper/2102.07074/) (2021)
- [Spherical Transformer for LiDAR-based 3D Recognition](https://mlsanity.com/paper/2303.12766/) (2023)
- [Attention on the Sphere](https://mlsanity.com/paper/2505.11157/) (2025)
- [HRTF upsampling with a generative adversarial network using a gnomonic equiangular projection](https://mlsanity.com/paper/2306.05812/) (2023)
- [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)
- [Head-Related Transfer Function Interpolation from Spatially Sparse Measurements Using Autoencoder with Source Position Conditioning](https://mlsanity.com/paper/2207.10967/) (2022)
- [HRTF Field: Unifying Measured HRTF Magnitude Representation with Neural Fields](https://mlsanity.com/paper/2210.15196/) (2022)
- [HRTF Individualization: A Survey](https://mlsanity.com/paper/2003.06183/) (2020)
- [Global HRTF Interpolation via Learned Affine Transformation of Hyper-conditioned Features](https://mlsanity.com/paper/2204.02637/) (2022)
- [Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization](https://mlsanity.com/paper/2501.13017/) (2025)
- [Modeling of Individual HRTFs based on Spatial Principal Component Analysis](https://mlsanity.com/paper/1910.09484/) (2019)
- [Machine Learning in Acoustics: A Review and Open-Source Repository](https://mlsanity.com/paper/2507.04419/) (2025)
- [Head-Related Transfer Function Individualization Using Anthropometric Features and Spatially Independent Latent Representation](https://mlsanity.com/paper/2508.16176/) (2025)
- [HRTF measurement for accurate sound localization cues](https://mlsanity.com/paper/2203.03166/) (2022)
- [On The Relevance Of The Differences Between HRTF Measurement Setups For Machine Learning](https://mlsanity.com/paper/2212.04283/) (2022)
- [Spatial Upsampling of Head-Related Transfer Functions Using a Physics-Informed Neural Network](https://mlsanity.com/paper/2307.14650/) (2023)
- [A Machine Learning Approach for Denoising and Upsampling HRTFs](https://mlsanity.com/paper/2504.17586/) (2025)
- [The Extended SONICOM HRTF Dataset and Spatial Audio Metrics Toolbox](https://mlsanity.com/paper/2507.05053/) (2025)

## Abstract (arXiv)

Individual Head-Related Transfer Functions (HRTFs) are starting to be introduced in many commercial immersive audio applications and are crucial for realistic spatial audio rendering. However, one of the main hesitations regarding their introduction is that creating individual HRTFs is impractical at scale due to the complexities of the HRTF measurement process. To mitigate this drawback, HRTF spatial upsampling has been proposed with the aim of reducing the measurements required. While prior work has seen success with different machine learning (ML) approaches, these models often struggle with long-range preservation of local spatial variation patterns across neighbouring source directions and generalization at high upsampling factors. In this paper, we propose a novel transformer-based architecture for HRTF upsampling, leveraging the attention mechanism to better capture spatial correlations across the HRTF sphere. Working in the spherical harmonic (SH) domain, our model learns to reconstruct high-resolution HRTFs from sparse input measurements with significantly improved accuracy. To enhance spatial coherence, we introduce a neighbour dissimilarity loss that promotes magnitude smoothness, yielding more realistic upsampling. We evaluate our method using both perceptual localization models and objective spectral distortion metrics. Experiments show that our model outperforms existing methods across several evaluation metrics in generating realistic, high-fidelity HRTFs.