Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-15813, Iran.
J Neural Eng. 2021 Aug 20;18(4). doi: 10.1088/1741-2552/ac16b4.
. Speech perception in cocktail party scenarios has been the concern of a group of researchers who are involved with the design of hearing-aid devices.. In this paper, a new unified ear-EEG-based binaural speech enhancement system is introduced for hearing-impaired (HI) listeners. The proposed model, which is based on auditory attention detection (AAD) and individual hearing threshold (HT) characteristics, has four main processing stages. In the binaural processing stage, a system based on the deep neural network is trained to estimate auditory ratio masks for each of the speakers in the mixture signal. In the EEG processing stage, AAD is employed to select one ratio mask corresponding to the attended speech. Here, the same EEG data is also used to predict the HTs of listeners who participated in the EEG recordings. The third stage, called insertion gain computation, concerns the calculation of a special amplification gain based on individual HTs. Finally, in the selection-resynthesis-amplification stage, the attended speech signals of the target are resynthesized based on the selected auditory mask and then are amplified using the computed insertion gain.. The detection of the attended speech and the HTs are achieved by classifiers that are trained with features extracted from the scalp EEG or the ear EEG signals. The results of evaluating AAD and HT detection show high detection accuracies. The systematic evaluations of the proposed system yield substantial intelligibility and quality improvements for the HI and normal-hearingaudiograms.. The AAD method determines the direction of attention from single-trial EEG signals without access to audio signals of the speakers. The amplification procedure could be adjusted for each subject based on the individual HTs. The present model has the potential to be considered as an important processing tool to personalize the neuro-steered hearing aids.
鸡尾酒会场景中的语音感知一直是一组研究人员关注的问题,他们参与了助听器设备的设计。在本文中,我们提出了一种新的基于双耳脑电的统一双耳语音增强系统,用于听力障碍(HI)听众。所提出的模型基于听觉注意检测(AAD)和个体听力阈值(HT)特征,有四个主要处理阶段。在双耳处理阶段,基于深度神经网络的系统用于估计混合信号中每个说话者的听觉比掩蔽。在 EEG 处理阶段,采用 AAD 选择与被关注语音对应的一个比掩蔽。在这里,相同的 EEG 数据也用于预测参与 EEG 记录的听众的 HT。第三个阶段,称为插入增益计算,涉及基于个体 HT 的特殊放大增益的计算。最后,在选择-合成-放大阶段,根据所选听觉掩蔽对目标的被关注语音信号进行重合成,然后使用计算出的插入增益进行放大。被关注语音和 HT 的检测是通过使用从头皮 EEG 或耳 EEG 信号中提取的特征进行训练的分类器来实现的。AAD 和 HT 检测的评估结果显示出较高的检测准确率。对所提出系统的系统评估表明,对于 HI 和正常听力听力图,都能显著提高语音的可懂度和质量。AAD 方法通过使用单试 EEG 信号而无需访问扬声器的音频信号来确定注意方向。该放大过程可以基于个体 HT 为每个受试者进行调整。目前的模型有可能被视为个性化神经导向助听器的重要处理工具。