Evaluation of Individualized HRTFs in a 3D Shooter Game

Previous research stresses the importance of Head-Related Transfer Function (HRTF) individualization approaches for accurately locating sound sources in virtual 3D spaces. However, in the realm of interactive experiences, methods for assessing whether individualized HRTFs bring a benefit to the player experience are rarely investigated. Methods to improve spatial audio rendering are needed now than ever since Virtual Reality (VR) is becoming a mainstream technology for interactive experiences. This paper proposes a method of using in-game metrics to test the hypothesis that individualized HRTFs improve the experience of both expert and novice players in a First-Person Shooter (FPS) game on a desktop environment. The FPS game provides players with a localization task across three different audio renderings using the same acoustic spaces: stereo panning (control condition), generic binaural rendering, and individualized binaural rendering. Collected metrics from the game include localization error, spatial quality attributes, and an extensive questionnaire. The individualized HRTFs for each participant were synthesized using a hybrid structural model. The model employs a deep learning architecture to synthesize a pinna-related response from a pinna image, and combines it with a measured generic head-and-torso response. The interaural time difference (ITD) is then adjusted to match that of an HRTF dataset subject minimizing a localization error metric. The results show that the 22 participants performed significantly better in the localization task with their individualized HRTF. Increased localization accuracy with respect to the generic HRTF was recorded both in azimuth and elevation perception, and especially in the case of expert game players.