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RAMamba-Net: A Reliability-Aware and Mamba-Based Multimodal Fusion Network for Auditory Attention Detection — arXiv:2609.11372

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.11372
License (as archived): CC0-1.0
Archived: 2026-10-16T05:24:11.314Z
SHA-256 of the archived text: 41cb04520476bfcc4e1f9731fd3fd161357f57770ae3617167c428d96d1a0b85
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How to cite

RAMamba-Net: A Reliability-Aware and Mamba-Based Multimodal Fusion Network for Auditory Attention Detection — arXiv:2609.11372 — arXiv (en), https://arxiv.org/abs/2609.11372 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_87ea5495ceec (Metraipolis archive dc_87ea5495ceec, sha256 41cb04520476bfcc4e1f9731fd3fd161357f57770ae3617167c428d96d1a0b85)

BibTeX

@misc{dc87ea5495ceec,
  title = {RAMamba-Net: A Reliability-Aware and Mamba-Based Multimodal Fusion Network for Auditory Attention Detection — arXiv:2609.11372},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.11372},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_87ea5495ceec; sha256 41cb04520476bfcc4e1f9731fd3fd161357f57770ae3617167c428d96d1a0b85},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.11372. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Auditory attention decoding (AAD) identifies the attended speaker from physiological signals, supporting neuro-steered hearing devices and natural human-machine interaction. Electroencephalography (EEG) is the dominant modality for AAD but provides incomplete evidence in naturalistic audio-visual scenes, motivating EEG and electrooculography (EOG) fusion. Existing approaches remain limited by weak cross-modal interaction, inefficient temporal modeling, and low robustness to sample variations. To address the limitations, we propose RAMamba-Net, a reliability-aware Mamba-based multimodal fusion network for AAD. RAMamba-Net employs a Mamba-enhanced band-aware convolutional Transformer to capture band-specific EEG patterns and long-range temporal dynamics. A dual-branch temporal-spatial encoder models EOG temporal and inter-channel dependencies. Cross-modal attention enables explicit modality interaction. Then, a reliability-aware module is introduced to estimate sample-wise modality weights for feature and prediction consistency, thereby enhancing multimodal fusion. Experiments on two AAD benchmarks demonstrate that RAMamba-Net effectively exploits complementary EEG-EOG information, yielding accuracy gains of 5.76% over unimodal baselines, together with more robust decoding and discriminative representations. Further analyses show that explicit cross-modal interaction improves multimodal alignment, while the reliability-aware module suppresses unreliable modality evidence and is robust to signal perturbation and parameter variation.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-11 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.11372 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T05:17:52.012Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).