{'fraction_non_alphanumeric': 0.02702702702702703, 'fraction_numerical': 0.0033783783783783786, 'mean_word_length': 5.620111731843576, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Cognitive-driven Binaural LCMV Beamformer Using EEG-based Auditory Attention Decoding

Identifying the target speaker in hearing aid applications is an essential ingredient to improve speech intelligibility. 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. Aiming at enhancing the target speaker and suppressing the interfering speaker and ambient noise, in this paper we propose a cognitive-driven speech enhancement system, consisting of a direction-of-arrival (DOA) estimator, steerable beamformers and AAD. To preserve the spatial impression of the acoustic scene, which is important when intending to switch attention between speakers, the proposed system only partially suppresses the interfering speaker. The speech enhancement performance of the proposed system is evaluated in terms of the signal-to-interference-plus-noise ratio (SINR) improvement in anechoic and reverberant conditions. The experimental results show that the proposed system can obtain a considerably large SINR improvement (between 3.1 dB and 7.5 dB) in both conditions.