Auditory attention detection: Application in neuro-steered hearing aids

Abstract

State-of-the-art hearing prostheses are equipped with acoustic noise reduction algorithms to improve speech intelligibility. However, cocktail party scenarios with multiple speakers pose a major challenge since it is difficult for the algorithm to determine which speaker it should enhance. To address this problem, electroencephalography (EEG) signals can be used to perform auditory attention detection (AAD), i.e., to detect which speaker the listener is attending to. Taking a step further towards realization of a neuro-steered hearing prosthesis, we worked on AAD-assisted noise suppression in a competing-speakers scenario in the presence of babble noise. We use an EEG-informed AAD module in combination with a blind source separation algorithm to extract the per-speaker envelopes from the microphone recordings, as well as a multi-channel Wiener filter to extract the denoised speech signal(s). Using a new algorithm pipeline, we obtain better AAD accuracies, and a better robustness to variations in speaker positions and signal-to-noise ratios (SNR), compared to previously reported results. Furthermore, the algorithm allows to switch more swiftly to the other speaker’s stream when there is a switch in attention.