While deep learning has advanced speech enhancement (SE), effective phase modeling remains challenging, as conventional networks typically operate within a flat Euclidean feature space, which is not easy to model the underlying circular topology of the phase. To address this, we propose a magnitude-phase dual-stream framework that aligns the phase stream with its intrinsic circular geometry by enforcing Global Rotation Equivariance (GRE) characteristic. Specifically, we introduce a Magnitude-Phase Interactive Convolutional Module (MPICM) for modulus-based information exchange and a Hybrid-Attention Dual Feed-Forward Network (HADF) bottleneck for unified feature fusion, both of which are designed to preserve GRE in the phase stream. Comprehensive evaluations are conducted across phase retrieval, denoising, dereverberation, and bandwidth extension tasks to validate the superiority of the proposed method over multiple advanced baselines. Notably, the proposed architecture reduces Phase Distance by over 20\% in the phase retrieval task and improves PESQ by more than 0.1 in zero-shot cross-corpus denoising evaluations. The overall superiority is also established in universal SE tasks involving mixed distortions. Qualitative analysis further reveals that the learned phase features exhibit distinct periodic patterns, which are consistent with the intrinsic circular nature of the phase. The source code is available at https://github.com/wangchengzhong/GRE-Net.

翻译：尽管深度学习推动了语音增强（SE）技术的发展，但有效的相位建模仍是一项挑战，因为传统网络通常在平坦的欧几里得特征空间中运行，难以模拟相位的固有环形拓扑。为解决此问题，我们提出了一种幅度-相位双流框架，通过强制全局旋转等变性（GRE）特性，使相位流与其内在的圆环几何对齐。具体而言，我们引入了基于模值信息交换的幅度-相位交互卷积模块（MPICM）和用于统一特征融合的混合注意力双前馈网络（HADF）瓶颈层，两者均设计用于保持相位流中的GRE特性。我们在相位恢复、去噪、去混响及带宽扩展任务上进行了全面评估，验证了所提方法相对于多个先进基准的优越性。值得注意的是，所提出的架构在相位恢复任务中将相位距离降低了20%以上，并在零样本跨语料库去噪评估中将PESQ提升了超过0.1分。在涉及混合失真的通用SE任务中也确立了整体优越性。定性分析进一步揭示，学习的相位特征呈现明显的周期性模式，这与相位的固有环形本质一致。源代码已开源至https://github.com/wangchengzhong/GRE-Net。