Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening

ArXi:2509.02571v2 Announce Type: replace-cross This paper investigates continuous representations of steering vectors over frequency and microphone/source positions for augmented listening (e.g., spatial filtering and binaural rendering), enabling user-parameterized control of the reproduced sound field. Steering vectors have typically been used for representing the spatial response of a microarray as a function of the look-up direction. The basic algebraic representation of these quantities assuming an idealized environment cannot deal with the scattering effect of the sound field.