Reduction of Spatial Sampling Requirement in Sound-Based Synthesis

We study the problem of synthesizing the sound field at arbitrary locations and times from the recordings of an array of audio sensors. Given prior estimates of the locations and frequencies of the sound sources, such as those obtained using adaptive source localization, we characterize the spatio-temporal support of the sound field spectrum. This characterization allows the spatial sampling requirements to be reduced in comparison to when no prior estimates of the sources are utilized. We derive an adaptive interpolation kernel, based on the estimated spectral support, to reconstruct the sound-field function using measurements from sensors on a coarse spatial-sampling grid. Simulation results demonstrate the gain achieved in reduced sampling requirements by using the proposed adaptive interpolation approach.