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This paper introduces a new method referred to as KISS-GEV (for Keep It SuperSimple Generalized eigenvalue) beamforming. While GEV beamforming usuallyrelies on deep neural network for estimating target and noise time-frequencymasks, this method uses a signal processing approach based on the direction ofarrival (DoA) of the target. This considerably reduces the amount ofcomputations involved at test time, and works for speech enhancement in unseenconditions as there is no need to train a neural network with noisy speech. Theproposed method can also be used to separate speech from a mixture, providedthe speech sources come from different directions. Results also show that theproposed method uses the same minimal DoA assumption as Delay-and-Sumbeamforming, yet outperforms this traditional approach.

Lightweight Speech Enhancement in Unseen Noisy and Reverberant Conditions using KISS-GEV Beamforming