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English(EN) NeuroCodec: EEG-Guided Latent-Domain Generative Model for High-Fidelity Target Speaker Extraction

NeuroCodec 利用脑电图信号进行高级说话人提取

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 NeuroCodec，这是一种利用脑电图 (EEG) 信号指导过程的新型高保真目标说话人提取生成模型。与以往关注时频掩蔽的方法不同，NeuroCodec 在神经音频编解码器的潜在空间中运行，对空间脑电图依赖性进行建模并融合跨模态表示。该模型采用了多阶段训练课程，并在公共数据集上展示了卓越的性能，在听力实验中实现了比现有基线高得多的感知准确度。 AI

影响 这项研究通过提高在嘈杂环境中分离特定语音的能力，可能有助于开发更有效的助听设备。

排序理由 该集群包含一篇详细介绍新模型及其方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

NeuroCodec 利用脑电图信号进行高级说话人提取

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该集群包含一篇详细介绍新模型及其方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Julian Silva, Jaliya Kodithuwakku, Navindu Sirimanna, Shaveen Herath, Wageesha N. Manamperi, Joshua P. Kulasingham ·

    NeuroCodec: EEG-Guided Latent-Domain Generative Model for High-Fidelity Target Speaker Extraction

    arXiv:2610.29815v1 Announce Type: cross Abstract: Driven by recent advancements in hearing assistive devices, target speaker extraction (TSE) in multi-talker environments has drawn significant attention. To address this, hearing technologies identify attention using neural signal…

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