EEG-based Auditory Attention Decoding: Impact of Reverberation, Noise and Interference Reduction

To identify the attended speaker from single-trial EEG recordings in an acoustic scenario with two competing speakers, an auditory attention decoding (AAD) method has recently been proposed. The AAD method requires the clean speech signals of both the attended and the unattended speaker as reference signals for decoding. However, in practice only the binaural signals, containing several undesired acoustic components (reverberation, background noise and interference), and influenced by anechoic head-related transfer functions (HRTFs), are available. To generate appropriate reference signals for decoding from the binaural signals, it is important to understand the impact of these acoustic components on the AAD performance. In this paper, we investigate this impact for decoding several acoustic conditions (anechoic, reverberant, noisy, and reverberant-noisy) by using simulated speech signals in which different acoustic components have been reduced. The experimental results show that for obtaining a good decoding performance the joint suppression of reverberation, background noise and interference as undesired acoustic components is of great importance.