Localization Experiments with Reporting by Head Orientation: Statistical Framework and Case Study

Abstract

This paper is concerned with sound localization experiments in which subjects report the position of an active sound source by turning toward it. A statistical framework for the analysis of the data from this type of experiment is presented together with a case study from a largescale listening experiment. The statistical framework is based on a model that is robust to the presence of front/back confusions and random errors. Closed-form natural estimators are derived, and one-sample and two-sample statistical tests are presented. The framework is used to analyze the data of an auralized experiment undertaken by nearly nine hundred subjects. Results show that responses had a rightward bias and that speech was harder to localize than percussion sounds, which are results consistent with the literature. Results also show that it was harder to localize sound in a simulated room with high ceiling, despite having a higher direct-to-reverberant ratio than other simulated rooms.

Localization of a virtual wall by means of active echolocation by untrained sighted persons

“…However, considering the ensemble of responses under this assumption, the angular data contained a mixture of guess answers uniformly distributed across all possible angles, obtained when participants did not detect the reflection, answers located around the true location, obtained when participants detected the reflection, or even answers located at a mirrored location behind the participant in case of front-back mistakes. Given this kind of data, it is particularly suitable to use the model for azimuth localization proposed in [42], where the probability of responses is modeled with a probability density function f ⇥ :…”

“…where f 1,⇥ (✓; µ, ) is the probability density function of the correctly de- In the model proposed in [42], f 1,⇥ (✓; µ, ) is a von Mises distribution [43], which among the circular distributions is the one having properties similar to the normal distribution in the linear domain. The von Mises distribution has a probability density function…”

“…In addition, non-individualized HRTFs were used. These are believed to deliver lower localization accuracy in the median plane [56] compared to individualized HRTF data and introduce front/back reversals [57,42]. However, head tracking-controlled sound reproduction improves localization accuracy and reduces front/back reversals [58].…”

“…This results in a significantly larger dispersion around the mean (AIC: 1432). On the other hand, the von Mises and Uniform Mixture (vMUM) model (2), which has 4 parameters (p 1 , p 2 , µ and ), is capable of capturing both components (AIC: 1300).After obtaining the parameters of the vMUM model (2) from the observations via maximum likelihood estimation, parametric statistical tests for equality in means and concentration factors[42] were applied to assess di↵erences in angle between groups. In order to assess the di↵erences between p 1 or p unif for di↵erent data groups, two-proportions z-tests were used.Given the relatively innovative approach to analyze angular data following the vMUM model, we provide an alternative analysis based on a repeated measures ANOVA of the absolute unsigned angle to study di↵erences across distance conditions.…”

On the Relative Importance of Visual and Spatial Audio Rendering on VR Immersion

“…This is particularly advantageous in the context of real-time binaural rendering as it avoids the need to cull reflections to keep computational complexity bounded (Hacıhabiboğlu and Murtagh, 2008). Indeed, the method used here to generate a binaural output is to spatialise only the six directions associated with the firstorder reflections directly (De Sena et al, 2017), using the HTB renderer described above. The reverberation tail is thus spatialised according to the direction of the first-order reflections.…”