Modeling the perception of system errors in spherical microphone array auralizations
A prominent trend in spatial audio research is the realization of virtual acoustic environments based on binaural technology. This study estimates the perceptual influence of system errors on the binaural reproduction of spherical microphone array data for room simulation applications. Specifically, the impact of spatial aliasing, system noise, and microphone positioning errors is perceptually analyzed in a listening experiment using an auditory model. Perceptual and technical data are related by various predictive modeling techniques, which enable estimating the perceptual strength of system errors. The experimental data comprises spherical array simulations under free-field conditions and in two reflective environments, a dry and a reverberant shoebox-shaped room, using five different audio signals for auralization. Results show that error prediction is possible with high accuracy and low errors using nonlinear modeling techniques such as artificial neural networks.