AFA-Net: A Differential Attention Approach for Auditory Attention Detection

Philip H. Lee; Shreeram Suresh Chandra; Karan Thakkar; John H. L. Hansen

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arXiv · 2609.31402

AFA-Net: A Differential Attention Approach for Auditory Attention Detection

Philip H. Lee·Shreeram Suresh Chandra·Karan Thakkar·John H. L. Hansen

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Abstract

Auditory Attention Detection (AAD) utilizes electroencephalographic (EEG) signals to identify a target speaker in a multi-speaker environment. Despite considerable progress, existing deep learning architectures often lack explicit mechanisms for handling noisy EEG data. To address this limitation, we propose Auditory Focus Attention Networks (AFA-Net), a machine learning framework that replaces vanilla attention with a simple yet flexible differential attention mechanism to help focus on task-relevant neural activity. AFA-Net achieves an upward accuracy of 96.8% at the 2s decision window, while using substantially fewer parameters than most existing methods. To the best of our knowledge, AFA-Net is among the first frameworks to explicitly try to combat EEG noise to improve AAD.

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Philip H. Lee, Shreeram Suresh Chandra, Karan Thakkar, John H. L. Hansen. 2026-09-25. AFA-Net: A Differential Attention Approach for Auditory Attention Detection. https://arxiv.org/abs/2609.31402

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