FAConformer: Frequency-Aware Convolutional Transformer for Auditory Attention Decoding
Auditory attention decoding (AAD) aims to infer the attended speaker from neural responses in multi-speaker acoustic environments and is a key problem for neuro-steered hearing systems. Although recent studies have achieved encouraging progress, existing AAD models still do not fully exploit frequency domain electroencephalography (EEG) information. In particular, most approaches introduce multi-band information through handcrafted feature extraction or direct cross-band feature concatenation, which mainly exploit frequency information at a shallow level and may overlook band-specific patterns and cross-band interactions. To address these limitations, this paper proposes FAConformer, a fr...
| Authors | Ziwei Wang, Xingyi He, Tianwang Jia et al. |
|---|---|
| Primary category | eess.SP |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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