Objective . A hearing aid’s noise reduction algorithm cannot infer to which speaker the user intends to listen to. Auditory attention decoding (AAD) algorithms allow to infer this information from neural signals, which leads to the concept of neuro-steered hearing aids. We aim to evaluate and demonstrate the feasibility of AAD-supported speech enhancement in challenging noisy conditions based on electroencephalography recordings. Approach . The AAD performance with a linear versus a deep neural network (DNN) based speaker separation was evaluated for same-gender speaker mixtures using three different speaker positions and three different noise conditions. Main results . AAD results based on the linear approach were found to be at least on par and sometimes even better than pure DNN-based approaches in terms of AAD accuracy in all tested conditions. However, when using the DNN to support a linear data-driven beamformer, a performance improvement over the purely...

中文翻译：

---

线性与深度学习方法用于EEG信息注意解码的嘈杂语音分离。

目标。助听器的降噪算法无法推断用户打算听哪个扬声器。听觉注意解码（AAD）算法允许从神经信号中推断出此信息，这导致了神经控制助听器的概念。我们旨在评估和证明基于脑电图记录的AAD支持的语音增强在具有挑战性的嘈杂条件下的可行性。方法。使用三个不同的扬声器位置和三个不同的噪声条件，针对同性别的扬声器混合物评估了基于线性与深度神经网络（DNN）的扬声器分离的AAD性能。主要结果。在所有测试条件下，基于线性方法的AAD结果至少在同等程度上优于有时甚至优于基于纯DNN的方法。