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.

翻译：在复杂听觉环境中引导听觉注意力的一个有效方法依赖于听觉注意力解码（AAD），其目标是从神经记录中识别多说话人场景下被关注的语音流。基于神经夹带的AAD方法通常假设能够获取纯净语音信号和脑电图（EEG）信号，从而利用神经响应与注意刺激之间的低频相关性。本研究提出CA-TCN，一种直接对注意力说话人进行分类的因果-反因果时序卷积网络。该架构整合了卷积神经网络在序列处理任务中的多项最佳实践。关键创新在于，它通过分别采用具有相反时序方向且感受野不同的因果卷积与反因果卷积，显式地对齐听觉刺激与神经响应。通过与三种基线AAD模型的对比实验结果表明，CA-TCN在不同数据集和决策窗口下均持续提升了解码准确率：在受试者独立模型中，相比次优模型AADNet提升了0.5%至3.2%；在受试者特定模型中提升了0.8%至2.9%。此外，在六种评估设置中，有四种设置的最小预期切换持续时间分布差异具有统计显著性。除准确率外，该模型在不同条件下展现出空间鲁棒性——EEG空间滤波器在不同数据集间保持稳定模式。总体而言，本研究提出了一种准确且统一的AAD模型，在超越现有方法的同时兼顾了在线处理场景的实用性。这些发现推动了AAD技术的发展及其在真实系统中的应用。