Collection of Research Outputs
Survey of HRTF Dataset Use in Academia and Industry
Head-related transfer functions (HRTFs) are crucial for plausible binaural audio playback for virtual, augmented, and mixed-reality applications. In such applications, humans showed higher sound-localisation accuracy, higher perceived externalisation, and experience less colouration when using their individual HRTFs compared to non-individual HRTFs. Because high-quality individual HRTFs require cumbersome measurements in specialised facilities, applications often use non-indivdual or dummy-head HRTFs as a practical alternative. Humans are able to adapt to non-individual HRTFs, which leads to a localisation performance comparable to that achieved with individual HRTFs. Therefore, adaptation to non-individual HRTFs could be a practical alternative whenever individual HRTFs are unavailable; However, this would only be possible if the same non-individual standard HRTF was used across different applications. To find out if this is the case, we conducted a survey on HRTF usage among 76 professionals working in the field of spatial audio. The findings suggest that there is currently no de facto standard HRTF. Surprisingly, only half of those with access to individual HRTFs are actually using them, and most would be willing to switch to a default HRTF set if one was established.
Comparing Measures of Information in Head-Related Transfer-Functions
The NEMO initiative works towards agreeing on one set of head-related transfer functions (HRTFs) for use across various applications whenever incorporating individual(ized) HRTFs is not feasible. This initiative is grounded on the assumption that listeners benefit from adapting to a set of HRTFs that is different from their own. Naturally, one key step in this process is the selec- tion of the particular set. We hypothesize that the available information in the set influences adaptation speed and/or post-adaptation localization performance and, therefore, is a potential factor in the selection. To test this hypothesis, it is essential to develop measures that quantify the information of an HRTF set. To this end, this contribution presents several potential measures of information content and compares them using multiple HRTF sets.
Toward a Standard Listener-Independent HRTF to Facilitate Long-Term Adaptation
Head-related transfer functions (HRTFs) are used in auditory applications for spatializing virtual sound sources. Listener-specific HRTFs, which aim at mimicking the filtering of the head, torso and pinnae of a specific listener, improve the perceived quality of virtual sound compared to using non-individualized HRTFs. However, using listener-specific HRTFs may not be accessible for everyone. Here, we propose as an alternative to take advantage of the adaptation abilities of human listeners to a new set of HRTFs. We claim that agreeing upon a single listener-independent set of HRTFs has beneficial effects for long-term adaptation compared to using several, potentially severely different HRTFs. Thus, the Non-individual Ear MOdel (NEMO) initiative is a first step towards a standardized listener-independent set of HRTFs to be used across applications as an alternative to individualization. A prototype, NEMObeta, is presented to explicitly encourage external feedback from the spatial audio community, and to agree on a complete list of requirements for the future HRTF selection.