English(EN) TF-MoE: Time-Frequency Mixture-of-Experts for Efficient Speech Separation

新型TF-MoE语音分离模型优化用于边缘设备

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-30 04:00

研究人员推出了一种新颖的稀疏专家混合（Mixture-of-Experts）框架TF-MoE，旨在改进用于边缘设备的语音分离模型。该方法在时间和频率维度上使用动态专家专业化，从而在对推理成本影响极小的情况下增加模型容量。TF-MoE基于Conformer骨干网络构建，在低计算场景下表现出卓越的性能，在Libri2Mix等基准测试中优于BSRNN等现有方法，同时保持可比的计算效率。 AI

影响该模型可以在资源受限的设备上实现更复杂的语音处理，扩展AI在移动和嵌入式应用中的能力。

排序理由该集群包含一篇详细介绍新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Qinzhe Hu, Chenda Li, Wangyou Zhang, Shujie Liu, Yan Lu, Yanmin Qian · 2026-06-30 04:00

TF-MoE：用于高效语音分离的时频混合专家模型

arXiv:2606.29575v1 Announce Type: cross Abstract: Recent advances in speech separation (SS) have led to compact front-end models with small parameter sizes, yet their high computational cost remains a major barrier for deployment on edge devices. To address this, we propose TF-Mo…

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报道来源 [1]

TF-MoE：用于高效语音分离的时频混合专家模型

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