Linear versus deep learning methods for noisy speech separation for EEG-informed attention decoding.

Journal: Journal of neural engineering

Published Date: Aug 19, 2020

Abstract

OBJECTIVE: A hearing aid's noise reduction algorithm cannot infer to which speaker the user intends to listen to. Auditory attention decoding (AAD) algorithms allow to infer this information from neural signals, which leads to the concept of neuro-steered hearing aids. We aim to evaluate and demonstrate the feasibility of AAD-supported speech enhancement in challenging noisy conditions based on electroencephalography recordings.

Authors

Neetha Das

KU Leuven, Deptartment Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven B-3001, Belgium. KU Leuven, Deptartment Neurosciences, ExpORL, Leuven B-3000, Belgium.
Jeroen Zegers
Hugo Van Hamme
Tom Francart
Alexander Bertrand

Keywords

Acoustic Stimulation Attention Deep Learning Electroencephalography Speech Speech Perception

External Resources

View on PubMed Access via DOI PubMed (32679578)

Linear versus deep learning methods for noisy speech separation for EEG-informed attention decoding.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals