  Acoustic beamformers have been widely used to enhance audio signals. The best
current methods are DNN-powered variants of the generalized eigenvalue
beamformer, and DNN-based filterestimation methods that directly compute
beamforming filters. Both approaches, while effective, have blindspots in their
generalizability. We propose a novel approach that combines both approaches
into a single framework that attempts to exploit the best features of both. The
resulting model, called a W-Net beamformer, includes two components: the first
computes a noise-masked reference which the second uses to estimate beamforming
filters. Results on data that include a wide variety of room and noise
conditions, including static and mobile noise sources, show that the proposed
beamformer outperforms other methods in all tested evaluation metrics.
