EEG-Based Auditory Attention Decoding Using Steerable Binaural Superdirective Beamformer
Ali Aroudi, Daniel Marquardt, Simon Doclo
Abstract
During the last decades significant progress in multi-microphone speech enhancement algorithms has been made for hearing aids. However, the performance of many algorithms depends on identifying the target speaker to be enhanced. To identify the target speaker from single-trial EEG recordings in an acoustic scenario with two competing speakers, an auditory attention decoding (AAD) method was recently proposed. This AAD method however requires the clean speech signals of both the attended and the unattended speaker as reference signals for decoding. Since in practice only microphone signals, containing several undesired acoustic components, are available, in this paper we explore the potential of using steerable binaural superdirective beamformer for generating appropriate reference signals for decoding. The experimental results show that using steerable superdirective beamformer output signals improves the decoding performance compared to using the noisy microphone signals as reference signals.
BibTeX
@inproceedings{icassp2018_eegbasedauditory,
title = {EEG-Based Auditory Attention Decoding Using Steerable Binaural Superdirective Beamformer},
author = {Ali Aroudi and Daniel Marquardt and Simon Doclo},
booktitle = {ICASSP 2018},
year = {2018}
}