# HRTF Individualization: A Survey

arXiv: https://arxiv.org/abs/2003.06183 · HTML: https://mlsanity.com/paper/2003.06183/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [HRTF Interpolation using a Spherical Neural Process Meta-Learner](https://mlsanity.com/paper/2310.13430/) (2023)
- [On The Relevance Of The Differences Between HRTF Measurement Setups For Machine Learning](https://mlsanity.com/paper/2212.04283/) (2022)
- [HRTFformer: A Spatially-Aware Transformer for Individual HRTF Upsampling in Immersive Audio Rendering](https://mlsanity.com/paper/2510.01891/) (2025)

## Abstract (arXiv)

The individuality of head-related transfer functions (HRTFs) is a key issue for binaural synthesis. While, over the years, a lot of work has been accomplished to propose end-user-friendly solutions to HRTF personalization, it remains a challenge. In this article we establish a state-of-the-art of that work. We classify the various proposed methods, review their respective advantages and disadvantages, and, above all, methodically check if and how the perceptual validity of the resulting HRTFs was assessed.