Das Neetha, Van Eyndhoven Simon, Francart Tom, Bertrand Alexander
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:77-80. doi: 10.1109/EMBC.2016.7590644.
State-of-the-art hearing prostheses are equipped with acoustic noise reduction algorithms to improve speech intelligibility. Currently, one of the major challenges is to perform acoustic noise reduction in so-called cocktail party scenarios with multiple speakers, in particular because it is difficult-if not impossible-for the algorithm to determine which are the target speaker(s) that should be enhanced, and which speaker(s) should be treated as interfering sources. Recently, it has been shown that electroencephalography (EEG) can be used to perform auditory attention detection, i.e., to detect to which speaker a subject is attending based on recordings of neural activity. In this paper, we combine such an EEG-based auditory attention detection (AAD) paradigm with an acoustic noise reduction algorithm based on the multi-channel Wiener filter (MWF), leading to a neuro-steered MWF. In particular, we analyze how the AAD accuracy affects the noise suppression performance of an adaptive MWF in a sliding-window implementation, where the user switches his attention between two speakers.
最先进的听力假体配备了声学降噪算法，以提高语音清晰度。目前，主要挑战之一是在有多个说话者的所谓鸡尾酒会场景中进行声学降噪，特别是因为算法很难（如果不是不可能的话）确定哪些是应该增强的目标说话者，哪些说话者应被视为干扰源。最近，研究表明脑电图（EEG）可用于进行听觉注意力检测，即根据神经活动记录检测受试者正在关注哪个说话者。在本文中，我们将这种基于EEG的听觉注意力检测（AAD）范式与基于多通道维纳滤波器（MWF）的声学降噪算法相结合，从而得到一种神经引导的MWF。特别是，我们分析了在滑动窗口实现中，当用户在两个说话者之间切换注意力时，AAD准确性如何影响自适应MWF的噪声抑制性能。
