3 papers
eess.AS2020
DBNET: DOA-driven beamforming network for end-to-end farfield sound source separation
Ali Aroudi, Sebastian Braun
Many deep learning techniques are available to perform source separation and reduce background noise. However, designing an end-to-end multi-channel source separation method using…
cs.SD2020
Cognitive-driven convolutional beamforming using EEG-based auditory attention decoding
Ali Aroudi, Marc Delcroix, Tomohiro Nakatani +3
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) m…
eess.AS2020
Improving auditory attention decoding performance of linear and non-linear methods using state-space model
Ali Aroudi, Tobias de Taillez, Simon Doclo
Identifying the target speaker in hearing aid applications is crucial to improve speech understanding. Recent advances in electroencephalography (EEG) have shown that it is possibl…