  This paper introduces a new method referred to as KISS-GEV (for Keep It Super
Simple Generalized eigenvalue) beamforming. While GEV beamforming usually
relies on deep neural network for estimating target and noise time-frequency
masks, this method uses a signal processing approach based on the direction of
arrival (DoA) of the target. This considerably reduces the amount of
computations involved at test time, and works for speech enhancement in unseen
conditions as there is no need to train a neural network with noisy speech. The
proposed method can also be used to separate speech from a mixture, provided
the speech sources come from different directions. Results also show that the
proposed method uses the same minimal DoA assumption as Delay-and-Sum
beamforming, yet outperforms this traditional approach.
