2021
DOI: 10.1051/aacus/2021019
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On the improvement of accommodation to non-individual HRTFs via VR active learning and inclusion of a 3D room response

Abstract: This study examines the efficiency of a training protocol using a virtual reality application designed to accelerate individual’s selection of, and accommodation to, non-individualized HRTF profiles. This training introduces three elements to hasten audio localization performance improvement: an interactive HRTF selection method, a parametric training program based on active learning, and a relatively dry room acoustic simulation designed to increase the quantity of spatial cues presented. Participants rapidly… Show more

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Cited by 10 publications
(23 citation statements)
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“…While the evaluation of similarity between HRTF assessments across the full rating scale is appropriate for investigating the effect of post-processing modifications on the perceived spatial quality of HRTFs, it is also informative to focus on specific ranges in the rating scale. Admittedly most evaluation/selection protocols focus on the identification of the "best-rated" HRTFs, disregarding "mediocre" or "unfit" data [46,47,36,11]. Additionally the use of a coarser rating scale has been shown to boost similarity in HRTF assessments [31].…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…While the evaluation of similarity between HRTF assessments across the full rating scale is appropriate for investigating the effect of post-processing modifications on the perceived spatial quality of HRTFs, it is also informative to focus on specific ranges in the rating scale. Admittedly most evaluation/selection protocols focus on the identification of the "best-rated" HRTFs, disregarding "mediocre" or "unfit" data [46,47,36,11]. Additionally the use of a coarser rating scale has been shown to boost similarity in HRTF assessments [31].…”
Section: Discussionmentioning
confidence: 99%
“…Nevertheless, even for cases when personalized HRTFs are available, they are hardly ever used without any type of processing. Whether performed to ensure compatibility between datasets from different databases [8], reduce the complexity in high-resolution HRTF spectra to perceptually relevant information [9] and lower the high dimensionality of the data [10], or be used in VR applications [11] and/or over the Web [12], HRTF post-processing is a reality that alters some aspects of the original measured data. Most often, while such actions affect the frequency and phase spectra of the data in a destructive manner, they are designed to constrain such artifacts to be within levels that are considered "acceptable" based on past research.…”
Section: Introductionmentioning
confidence: 99%
“…It has been established that one can adapt to modified HRTFs over time, with ear moulds inserted in the pinnae [19,38,94,95], or with non-individual HRTFs through binaural rendering [73,77,90,92,99,109]. Studies have shown that one can adapt to distorted HRTFs, e.g.…”
Section: User-to-system Adaptation: Hrtf Accommodationmentioning
confidence: 99%
“…HRTF learning is not only possible, but lasting in time [62,92,109]: users have been shown to retain performance improvements up to 4 months after training [109]. Given enough time, participants using non-individual HRTFs may achieve localisation performance on par with participants using their own individual HRTFs [73,77,92].…”
Section: User-to-system Adaptation: Hrtf Accommodationmentioning
confidence: 99%
“…Through such simulations, and subsequent "auralization" of the simulated room acoustic, it is then possible to render audible the acoustics of a computer simulated space [53,54]. While employed in the acoustic design of buildings [55,56], the auralization of simulated room acoustics is also at the centre of high quality virtual reality simulations which have been used, in, e.g., multimodal perception research [57,58] and historical reconstructions [59][60][61]. These techniques of binaural audio and room acoustic auralization are employed in several of the studies presented here.…”
Section: Brief Introduction To Auditory Virtual Realitymentioning
confidence: 99%