Beamforming with Partial Knowledge of the Acoustic Scenario

W. Bastiaan Kleijn, Christopher Laguna, Alejandro Luebs, Andrew MacDonald, Jan Skoglund

Abstract: We address the problem of acoustic beamforming with a small number of microphones given only limited knowledge of the spatial scenario. We first identify a set of plausible target (desired source) scenarios and a set of interferer scenarios that have desirable suppression characteristics. We then design soft masks (postprocessors) for all target-interferer scenario pairs and select the composite scenario that maximizes the output variance over target scenarios and minimizes it over interferer scenarios. This corresponds to an approximate concatenation of soft masks. The result is a nonlinear beamformer with an adjustable beamwidth and an adjustable region where point interferers are strongly suppressed, even for two microphones. For the individual masks we use a new postprocessor formulation that is robust to scenario mismatch. The resulting system provides excellent performance with few microphones even when the acoustic scenario is ill-defined.