Multi-channel speech separation in dynamic environments is challenging as time-varying spatial and spectral features evolve at different temporal scales. Existing methods typically employ sequential architectures, forcing a single network stream to simultaneously model both feature types, creating an inherent modeling conflict. In this paper, we propose a dual-branch parallel spectral-spatial (PS2) architecture that separately processes spectral and spatial features through parallel streams. The spectral branch uses a bi-directional long short-term memory (BLSTM)-based frequency module, a Mamba-based temporal module, and a self-attention module to model spectral features. The spatial branch employs bi-directional gated recurrent unit (BGRU) networks to process spatial features that encode the evolving geometric relationships between sources and microphones. Features from both branches are integrated through a cross-attention fusion mechanism that adaptively weights their contributions. Experimental results demonstrate that the PS2 outperforms existing state-of-the-art (SOTA) methods by 1.6-2.2 dB in scale-invariant signal-to-distortion ratio (SI-SDR) for moving speaker scenarios, with robust separation quality under different reverberation times (RT60), noise levels, and source movement speeds. Even with fast source movements, the proposed model maintains SI-SDR improvements of over 13 dB. These improvements are consistently observed across multiple datasets, including WHAMR! and our generated WSJ0-Demand-6ch-Move dataset.

翻译：动态环境中的多通道语音分离具有挑战性，因为时变的空域和谱域特征以不同的时间尺度演变。现有方法通常采用串行架构，迫使单一网络流同时建模两种特征类型，从而产生固有的建模冲突。本文提出一种双分支并行谱-空（PS2）架构，通过并行流分别处理谱域和空域特征。谱域分支使用基于双向长短期记忆（BLSTM）的频率模块、基于Mamba的时序模块和自注意力模块来建模谱域特征。空域分支采用双向门控循环单元（BGRU）网络处理空域特征，这些特征编码了声源与麦克风之间不断演变的几何关系。两个分支的特征通过交叉注意力融合机制进行整合，该机制自适应地加权它们的贡献。实验结果表明，在动态说话人场景下，PS2在尺度不变信噪比（SI-SDR）上优于现有最先进（SOTA）方法1.6-2.2 dB，并在不同混响时间（RT60）、噪声水平和声源移动速度下均保持稳健的分离质量。即使在声源快速移动时，所提模型仍能维持超过13 dB的SI-SDR提升。这些改进在多个数据集上均得到一致验证，包括WHAMR!和我们生成的WSJ0-Demand-6ch-Move数据集。