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SEAL：混合闭合加性重建与感知路由的语音分离方法

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2026年10月7日，arXiv Audio and Speech（一手）发布SEAL（Sparse Expert routing with Additive Latent reconstruction），面向高效语音分离。该方法提出零和加性残差与六专家路由，用于解决时频掩码在成分抵消处估计偏小、共享单元计算随规模膨胀的问题。报道未给出实验数据、对比基线或代码开源信息。

Generated from reports · updated 3 hr ago

LatestOct 7
SEAL提出零和加性残差与六专家路由，arXiv已发布。

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Oct 7, 2026
  1. arXiv · Audio and Speech
    SEAL：面向高效语音分离的混合闭合加性重建与感知路由专家

    SEAL（Sparse Expert routing with Additive Latent reconstruction）提出零和加性残差与六专家路由，以解决时频掩码在成分抵消处估计偏小、共享单元计算随规模膨胀的问题。

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