While previous research on spatial sound perception investigated the physical mechanisms producing the most relevant elevation cues, how spectral notches are generated and related to the individual morphology of the human pinna is still a topic of debate. Correctly modeling these important elevation cues, and in particular the lowest frequency notches, is an essential step for individualizing Head-Related Transfer Functions (HRTFs). In this paper we propose a simple computational model able to predict the center frequencies of pinna notches from ear meshes. We apply such a model to a highly controlled HRTF dataset built with the specific purpose of understanding the contribution of the pinna to the HRTF. Results show that the computational model is able to approximate the lowest frequency notch with improved accuracy with respect to other state-of-the-art methods. By contrast, the model fails to predict higher-order pinna notches correctly. The proposed approximation supplements understanding of the morphology involved in generating spectral notches in experimental HRTFs.@en

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.@en