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Assessing the Potential Impact of Direction-Dependent HRTF Selection on Sound Localization Accuracy

Sapir Goldring, Zamir Ben Hur, David Lou Alon, Boaz Rafaely

TL;DR

This paper investigates whether selecting HRTFs in a direction-dependent manner can enhance sound localization without individualized measurements. By reducing a large HRTF database to five perceptually diverse filters based on the first spectral notch and evaluating three selection strategies in a VR localization task, the study quantifies localization accuracy across elevation and azimuth. The main finding is a notable reduction in elevation localization error (~5.6°) when employing direction-dependent HRTF selection, while azimuth improvements are modest. These results suggest practical benefits for VR and spatial audio applications, and point to future work on mixed-HRTF approaches and interpolation to further improve localization without individualized measurements.

Abstract

This study investigates the approach of direction-dependent selection of Head-Related Transfer Functions (HRTFs) and its impact on sound localization accuracy. For applications such as virtual reality (VR) and teleconferencing, obtaining individualized HRTFs can be beneficial yet challenging, the objective of this work is therefore to assess whether incorporating HRTFs in a direction-dependent manner could improve localization precision without the need to obtain individualized HRTFs. A localization experiment conducted with a VR headset assessed localization errors, comparing an overall best HRTF from a set, against selecting the best HRTF based on average performance in each direction. The results demonstrate a substantial improvement in elevation localization error with the method motivated by direction-dependent HRTF selection, while revealing insignificant differences in azimuth errors.

Assessing the Potential Impact of Direction-Dependent HRTF Selection on Sound Localization Accuracy

TL;DR

This paper investigates whether selecting HRTFs in a direction-dependent manner can enhance sound localization without individualized measurements. By reducing a large HRTF database to five perceptually diverse filters based on the first spectral notch and evaluating three selection strategies in a VR localization task, the study quantifies localization accuracy across elevation and azimuth. The main finding is a notable reduction in elevation localization error (~5.6°) when employing direction-dependent HRTF selection, while azimuth improvements are modest. These results suggest practical benefits for VR and spatial audio applications, and point to future work on mixed-HRTF approaches and interpolation to further improve localization without individualized measurements.

Abstract

This study investigates the approach of direction-dependent selection of Head-Related Transfer Functions (HRTFs) and its impact on sound localization accuracy. For applications such as virtual reality (VR) and teleconferencing, obtaining individualized HRTFs can be beneficial yet challenging, the objective of this work is therefore to assess whether incorporating HRTFs in a direction-dependent manner could improve localization precision without the need to obtain individualized HRTFs. A localization experiment conducted with a VR headset assessed localization errors, comparing an overall best HRTF from a set, against selecting the best HRTF based on average performance in each direction. The results demonstrate a substantial improvement in elevation localization error with the method motivated by direction-dependent HRTF selection, while revealing insignificant differences in azimuth errors.
Paper Structure (10 sections, 4 figures, 1 table)

This paper contains 10 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. HRTF dataset
  4. Localization Experiment setup
  5. Results
  6. Three methods for HRTF Selection
  7. Statistical Analysis
  8. Directional Variability of Selected HRTFs
  9. Discussion and Conclusion
  10. Acknowledgments

Figures (4)

  • Figure 1: Elevation errors of subject 1
  • Figure 2: Elevation error in degrees for the three HRTF selection methods
  • Figure 3: Azimuth error in degrees for the three HRTF selection methods
  • Figure 4: Averaged, ordered distribution of HRTFs based on their selection as the optimal fit across different directions. The x-axis represents the rank order of HRTFs by their frequency of being chosen as the best fit, from the most to the least common.