A framework for designing head-related transfer function distance metrics that capture localization perception

Linear comparisons can fail to describe perceptual differences between head-related transfer functions (HRTFs), reducing their utility for perceptual tests, HRTF selection methods, and prediction algorithms. This work introduces a machine learning framework for constructing a perceptual error metric that is aligned with performance in human sound localization. A neural network is first trained to predict measurement locations from a large database of HRTFs and then fine-tuned with perceptual data. It demonstrates robust model performance over a standard spectral difference error metric. A statistical test is employed to quantify the information gain from the perceptual observations as a function of space.