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

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.

Sound (cs.SD); Machine Learning (cs.LG); Signal Processing (eess.SP)

arXiv:2609.31402 [cs.SD]

(or arXiv:2609.31402v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2609.31402 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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