论文标题
使用基于脑电图的听觉注意力解码的认知驱动卷积波束形成
Cognitive-driven convolutional beamforming using EEG-based auditory attention decoding
论文作者
论文摘要
多演讲者场景中语音增强算法的性能取决于正确识别目标扬声器以增强。听觉注意力解码（AAD）方法允许识别侦听器从单审eeg录音中参与的目标扬声器。旨在增强目标扬声器并抑制干扰扬声器，混响和环境噪音，在本文中，我们提出了一种认知驱动的多微粒语音语音增强系统，该系统结合了基于神经网络的掩码估计器，加权的最小功率无畸变反应卷积器和AAD。为了控制抑制干扰扬声器的抑制，我们还提出了一个延伸，其中包含了干扰抑制约束。实验结果表明，在具有挑战性的混响和嘈杂条件下，提出的系统优于最先进的认知驱动语音增强系统。
The performance of speech enhancement algorithms in a multi-speaker scenario depends on correctly identifying the target speaker to be enhanced. Auditory attention decoding (AAD) methods allow to identify the target speaker which the listener is attending to from single-trial EEG recordings. Aiming at enhancing the target speaker and suppressing interfering speakers, reverberation and ambient noise, in this paper we propose a cognitive-driven multi-microphone speech enhancement system, which combines a neural-network-based mask estimator, weighted minimum power distortionless response convolutional beamformers and AAD. To control the suppression of the interfering speaker, we also propose an extension incorporating an interference suppression constraint. The experimental results show that the proposed system outperforms the state-of-the-art cognitive-driven speech enhancement systems in challenging reverberant and noisy conditions.
