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arXiv Machine Learning By Ali Alavi, Donald S. Williamson

NEUROTOKEN: Joint Source and Directional AAD with Envelope Decoding via Conditional Flow Matching

Read the original on arXiv Machine Learning →

The paper introduces NEUROTOKEN, a unified neural network for auditory attention decoding (AAD) that jointly predicts the attended speaker’s direction and source by modeling the conditional likelihood of the attended envelope given EEG. It employs a conditional flow‑matching head (ATTUNEFLOW) and two inference‑time ensembles (QUADTRACK and ENV‑FLOW) to improve source‑AAD accuracy and reduce variance across subjects. Experiments on KU Leuven, DTU, and NJU datasets show significant gains over existing baselines and reveal that prior direction‑AAD results overestimate performance under stricter protocols.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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