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End-to-End Brain-Driven Speech Enhancement in Multi-Talker Conditions

Single-channel speech enhancement algorithms have seen great improvements over the past few years. Despite these improvements, they still lack the efficiency of the auditory system in extracting attended auditory information in the presence of competing speakers. Recently, it has been shown that the attended auditory information can be decoded from the brain activity of the listener. In this paper, we propose two novel end-to-end deep learning methods referred to as the Brain Enhanced Speech Denoiser (BESD) and the U-shaped Brain Enhanced Speech Denoiser (U-BESD) respectively, that take advantage of this fact to denoise a multi-talker speech mixture without considering further background noises or reverberations. We use a Feature-wise Linear Modulation (FiLM) between the brain activity and the sound mixture, to better extract the features of the attended speaker to perform speech enhancement. We show, using electroencephalography (EEG) signals recorded from the listener, that both BESD and U-BESD successfully extract the attended speaker without any prior information about this speaker. Moreover, U-BESD also outperforms a current state-of-the-art approach that also uses brain activity to perform enhancement. The proposed neural network-based methods would thus make great candidates for realistic applications where no prior information about the attended speaker is available, such as hearing aids, cellphones, or noise cancelling headphones.