  The performance of speech enhancement algorithms in a multi-speaker scenario
depends on correctly identifying the target speaker to be enhanced. Auditory
attention decoding (AAD) methods allow to identify the target speaker which the
listener is attending to from single-trial EEG recordings. Aiming at enhancing
the target speaker and suppressing interfering speakers, reverberation and
ambient noise, in this paper we propose a cognitive-driven multi-microphone
speech enhancement system, which combines a neural-network-based mask
estimator, weighted minimum power distortionless response convolutional
beamformers and AAD. To control the suppression of the interfering speaker, we
also propose an extension incorporating an interference suppression constraint.
The experimental results show that the proposed system outperforms the
state-of-the-art cognitive-driven speech enhancement systems in challenging
reverberant and noisy conditions.
