Impact of auditory attention decoding accuracy on noise reduction systems for hearing aids (2026)
摘要
Hearing aid users often struggle to focus on a specific target speaker in multi-talker environments. Auditory attention decoding (AAD) algorithms, which extract attentional cues from electroencephalogram (EEG) signals, offer a potential solution. This study evaluates how AAD accuracy and decision window length affect the performance of a multichannel Wiener filter noise reduction system in a speaker and story-independent scenario. Simulations in two-speaker anechoic conditions show that, for decision windows of 1 s or less, AAD accuracies approximately above 81 % are required to meet minimum conversational speech quality (PESQ = 2.0), while accuracies approximately above 64 % suffice for intelligibility (STOI = 0.62). These results define quantitative performance targets for integrating AAD-based noise reduction into hearing aids and highlight the trade-off between decision latency, decoding accuracy, and perceptual benefit under idealized beamforming/VAD and anechoic conditions with high-density EEG.
