Abstract
Individualization of head-related transfer functions (HRTFs) can improve the quality of binaural applications with respect to the localization accuracy, coloration, and other aspects. Using anthropometric features (AFs) of the head, neck, and pinna for individualization is a promising approach to avoid elaborate acoustic measurements or numerical simulations. Previous studies on HRTF individualization analyzed the link between AFs and technical HRTF features. However, the perceptual relevance of specific errors might not always be clear. Hence, the effects of AFs on perceived perceptual qualities with respect to the overall difference, coloration, and localization error are directly explored. To this end, a listening test was conducted in which subjects rated differences between their own HRTF and a set of nonindividual HRTFs. Based on these data, a machine learning model was developed to predict the perceived differences using ratios of a subject's individual AFs and those of presented nonindividual AFs. Results show that perceived differences can be predicted well and the HRTFs recommended by the models provide a clear improvement over generic or randomly selected HRTFs. In addition, the most relevant AFs for the prediction of each type of error were determined. The developed models are available under a free cultural license.
Highlights
The influence of the human head, torso, and pinnae on acoustic signals arriving at the eardrum is described by headrelated transfer functions (HRTFs; Møller, 1992)
The raw ratings were scaled to the range between zero and one by a division by three, whereby a zero rating denotes no perceivable differences between a specific individual and nonindividual head-related transfer functions (HRTFs) and a rating of one denotes very large differences
The results show that some of the randomly presented nonindividual HRTFs were perceptually identical to the individual HRTF with respect to the tested qualities, whereas others were perceived as being very different
Summary
The influence of the human head, torso, and pinnae on acoustic signals arriving at the eardrum is described by headrelated transfer functions (HRTFs; Møller, 1992). The most precise approach to obtain individual HRTFs is by placing microphones in the ear canals and measuring the acoustic signals for a large set of source positions in an anechoic chamber (Brinkmann et al, 2019; Richter and Fels, 2019). Another approach is to run numerical simulations using individual high-resolution three-dimensional (3D) surface meshes (Dinakaran et al, 2018; Katz, 2001). Both methods require elaborate equipment and specific skills to perform the measurements. It seems appealing to find other HRTF individualization approaches
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