标题： 面向神经目标说话人的改进特征提取网络 显示英文标题

标题： Improved Feature Extraction Network for Neuro-Oriented Target Speaker Extraction

摘要： 最近听觉注意力解码（AAD）的快速发展为使用脑电图（EEG）作为目标说话人提取的辅助信息提供了可能性。 然而，有效建模语音的长序列并从EEG信号中解析目标说话人的身份仍然是一个主要挑战。 本文提出了一种改进的特征提取网络（IFENet），用于面向神经的目标说话人提取，该网络主要由一个具有双路径Mamba的语音编码器和一个具有Kolmogorov-Arnold网络（KAN）的EEG编码器组成。 我们提出了SpeechBiMamba，利用双路径Mamba对局部和全局语音序列进行建模以提取语音特征。 此外，我们提出了EEGKAN，以有效提取与听觉刺激密切相关的EEG特征，并通过受试者的注意力信息定位目标说话人。 在KUL和AVED数据集上的实验表明，IFENet优于最先进的模型，在开放评估条件下，尺度不变信噪比（SI-SDR）分别实现了36%和29%的相对提升。 显示英文摘要

摘要： The recent rapid development of auditory attention decoding (AAD) offers the possibility of using electroencephalography (EEG) as auxiliary information for target speaker extraction. However, effectively modeling long sequences of speech and resolving the identity of the target speaker from EEG signals remains a major challenge. In this paper, an improved feature extraction network (IFENet) is proposed for neuro-oriented target speaker extraction, which mainly consists of a speech encoder with dual-path Mamba and an EEG encoder with Kolmogorov-Arnold Networks (KAN). We propose SpeechBiMamba, which makes use of dual-path Mamba in modeling local and global speech sequences to extract speech features. In addition, we propose EEGKAN to effectively extract EEG features that are closely related to the auditory stimuli and locate the target speaker through the subject's attention information. Experiments on the KUL and AVED datasets show that IFENet outperforms the state-of-the-art model, achieving 36\% and 29\% relative improvements in terms of scale-invariant signal-to-distortion ratio (SI-SDR) under an open evaluation condition.