Abstract
A promising approach for steering auditory attention in complex listening environments relies on Auditory Attention Decoding (AAD), which aim to identify the attended speech stream in a multiple speaker scenario from neural recordings. Entrainment-based AAD approaches, typically assume access to clean speech sources and electroencephalography (EEG) signals to exploit low-frequency correlations between the neural response and the attended stimulus. In this study, we propose CA-TCN, a Causal-Anticausal Temporal Convolutional Network that directly classifies the attended speaker. The proposed architecture integrates several best practices from convolutional neural networks in sequence processing tasks. Importantly, it explicitly aligns auditory stimuli and neural responses by employing separate causal and anticausal convolutions respectively, with distinct receptive fields operating in opposite temporal directions. Experimental results, obtained through comparisons with three baseline AAD models, demonstrated that CA-TCN consistently improved decoding accuracy across datasets and decision windows, with gains ranging from 0.5% to 3.2% for subject-independent models and from 0.8% to 2.9% for subject-specific models compared with the next best-performing model, AADNet. Moreover, these improvements were statistically significant in four of the six evaluated settings when comparing Minimum Expected Switch Duration distributions. Beyond accuracy, the model demonstrated spatial robustness across different conditions, as the EEG spatial filters exhibited stable patterns across datasets. Overall, this work introduces an accurate and unified AAD model that outperforms existing methods while considering practical benefits for online processing scenarios. These findings contribute to advancing the state of AAD and its applicability in real-world systems.
Abstract (translated)
在复杂听觉环境中引导听觉注意力的一个前景方法依赖于听觉注意力解码（AAD），其旨在从多说话人场景的神经记录中识别被注意的语音流。基于锁相效应的AAD方法通常假设可获取纯净语音源和脑电图（EEG）信号，以利用神经响应与被注意刺激之间的低频相关性。本研究提出CA-TCN，一种因果-反因果时序卷积网络，可直接分类被注意的说话人。所提出的架构整合了序列处理任务中卷积神经网络的多项最佳实践。关键的是，该模型通过分别采用因果卷积与反因果卷积，以相反时间方向上具有不同感受野的方式，实现了听觉刺激与神经响应的显式对齐。通过与三种基线AAD模型对比获得的实验结果表明，CA-TCN在跨数据集和决策窗口的解码精度上均持续提升：相较于次优模型AADNet，其针对受试者独立模型的增益范围为0.5%至3.2%，针对受试者专属模型的增益范围为0.8%至2.9%。此外，在比较最小预期切换时长分布时，六个评估设置中的四个显示这些改进具有统计显著性。除精度外，该模型在不同条件下展现出空间鲁棒性，因为脑电空间滤波器在各数据集间呈现稳定模式。总体而言，本研究提出了一种精确且统一的AAD模型，在优于现有方法的同时兼顾了在线处理场景的实际效益。这些发现有助于推动AAD技术发展及其在真实系统中的适用性。
URL
https://arxiv.org/abs/2603.26394
