Examining the interrelation behavior of distance metrics for head-related transfer function evaluation: a case study

Abstract

The comparison of head-related transfer functions (HRTFs), e.g., for validating different acquisition methods, requires a meaningful way of quantifying HRTF differences – a problem to which literature offers no standardized approach. This impedes the comparability and interpretability of studies. The present work therefore addresses the lack of proper understanding of the behavior of commonly used distance metrics by applying seven metrics to individually measured and approximated HRTF datasets. Covering a variety of spectral deviations, we perform both intra-individual comparisons (contrasting different levels of spectral detail for the same individual) and inter-individual comparisons (assessing metric reactions to non-individual cue differences). The metrics exhibit selective reactions to distinct spectral alterations. Particularly, the results demonstrate that spectrally localized errors go undetected by five out of seven metrics. Further analysis emphasizes inconsistencies in metric correlation patterns. These observations highlight the need for a multi-dimensional metric, capturing various types of HRTF differences for a proper assessment of errors.