# On The Relevance Of The Differences Between HRTF Measurement Setups For Machine Learning

arXiv: https://arxiv.org/abs/2212.04283 · HTML: https://mlsanity.com/paper/2212.04283/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems](https://mlsanity.com/paper/2012.03448/) (2020)
- [HRTF Individualization: A Survey](https://mlsanity.com/paper/2003.06183/) (2020)
- [A Wide Dataset of Ear Shapes and Pinna-Related Transfer Functions Generated by Random Ear Drawings](https://mlsanity.com/paper/2003.06182/) (2020)

## Built on by (most cited first, 1 of 1)

- [HRTFformer: A Spatially-Aware Transformer for Individual HRTF Upsampling in Immersive Audio Rendering](https://mlsanity.com/paper/2510.01891/) (2025)

## Abstract (arXiv)

As spatial audio is enjoying a surge in popularity, data-driven machine learning techniques that have been proven successful in other domains are increasingly used to process head-related transfer function measurements. However, these techniques require much data, whereas the existing datasets are ranging from tens to the low hundreds of datapoints. It therefore becomes attractive to combine multiple of these datasets, although they are measured under different conditions. In this paper, we first establish the common ground between a number of datasets, then we investigate potential pitfalls of mixing datasets. We perform a simple experiment to test the relevance of the remaining differences between datasets when applying machine learning techniques. Finally, we pinpoint the most relevant differences.