Impact of Different Acoustic Components on EEG-based Auditory Attention Decoding in Noisy and Reverberant Conditions
Recently, a least-squares-based method has been proposed to decode auditory attention from single-trial EEG recordings for an acoustic scenario with two competing speakers. This method aims at reconstructing the attended speech envelope from the EEG recordings using a trained spatio-temporal filter. While the performance of this method has been mainly studied for noiseless and anechoic acoustic conditions, it is important to fully understand its performance in realistic noisy and reverberant acoustic conditions. In this paper, we investigate auditory attention decoding (AAD) using EEG recordings for different acoustic conditions (anechoic, reverberant, noisy, and reverberant-noisy). In particular, we investigate the impact of different acoustic conditions for AAD filter training and for decoding. In addition, we investigate the influence on the decoding performance of the different acoustic components (i.e. reverberation, background noise and interfering speaker) in the reference signals used for decoding and the training signals used for computing the filters. First, we found that for all considered acoustic conditions it is possible to decode auditory attention with a decoding performance larger than 90%, even when the acoustic conditions for AAD filter training and for decoding are different. Second, when using reference signals affected by reverberation and/or background noise, a comparable decoding performance as when using clean reference signals can be obtained. In contrast, when using reference signals affected by the interfering speaker, the decoding performance significantly decreases. Third, the experimental results indicate that it is even feasible to use training signals affected by reverberation, background noise and/or the interfering speaker for computing the filters.