# Head-Related Transfer Function Interpolation from Spatially Sparse Measurements Using Autoencoder with Source Position Conditioning

arXiv: https://arxiv.org/abs/2207.10967 · HTML: https://mlsanity.com/paper/2207.10967/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2022.

## Built on by (most cited first, 4 of 4)

- [HRTF upsampling with a generative adversarial network using a gnomonic equiangular projection](https://mlsanity.com/paper/2306.05812/) (2023)
- [HRTF Interpolation using a Spherical Neural Process Meta-Learner](https://mlsanity.com/paper/2310.13430/) (2023)
- [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)

## Abstract (arXiv)

We propose a method of head-related transfer function (HRTF) interpolation from sparsely measured HRTFs using an autoencoder with source position conditioning. The proposed method is drawn from an analogy between an HRTF interpolation method based on regularized linear regression (RLR) and an autoencoder. Through this analogy, we found the key feature of the RLR-based method that HRTFs are decomposed into source-position-dependent and source-position-independent factors. On the basis of this finding, we design the encoder and decoder so that their weights and biases are generated from source positions. Furthermore, we introduce an aggregation module that reduces the dependence of latent variables on source position for obtaining a source-position-independent representation of each subject. Numerical experiments show that the proposed method can work well for unseen subjects and achieve an interpolation performance with only one-eighth measurements comparable to that of the RLR-based method.