A Data-Driven Exploration of Elevation Cues in HRTFs: An Explainable AI Perspective Across Multiple Datasets
Journal:
arXiv
Published Date:
Mar 14, 2025
Abstract
Precise elevation perception in binaural audio remains a challenge, despite
extensive research on head-related transfer functions (HRTFs) and spectral
cues. While prior studies have advanced our understanding of sound localization
cues, the interplay between spectral features and elevation perception is still
not fully understood. This paper presents a comprehensive analysis of over 600
subjects from 11 diverse public HRTF datasets, employing a convolutional neural
network (CNN) model combined with explainable artificial intelligence (XAI)
techniques to investigate elevation cues. In addition to testing various HRTF
pre-processing methods, we focus on both within-dataset and inter-dataset
generalization and explainability, assessing the model's robustness across
different HRTF variations stemming from subjects and measurement setups. By
leveraging class activation mapping (CAM) saliency maps, we identify key
frequency bands that may contribute to elevation perception, providing deeper
insights into the spectral features that drive elevation-specific
classification. This study offers new perspectives on HRTF modeling and
elevation perception by analyzing diverse datasets and pre-processing
techniques, expanding our understanding of these cues across a wide range of
conditions.
