Dual-Path State-Space Modeling With Cross-Domain Interaction for Multichannel Speech Enhancement

We propose a novel dual-path state-space model within an encoder-decoder architecture for multichannel speech enhancement that leverages joint temporal and spectral modeling to significantly improve speech quality in noisy and reverberant environments. At the core of our framework is the S5 state-space model, which efficiently captures complex temporal dependencies across multiple speech channels by modeling both short and long-term dynamics. To effectively integrate spatial and spectral features, our encoder employs S3Conv layers that extract salient characteristics from the raw input, while a dedicated cross-domain interaction mechanism facilitates a dynamic exchange of information between two parallel data streams used for coarse magnitude estimation and complex spectral refinement, respectively. This dual-path design enables the network to jointly enhance amplitude and phase information, resulting in improved perceptual quality and intelligibility. Extensive experiments on public datasets demonstrate that our model outperforms state-of-the-art methods across multiple evaluation metrics. Ablation studies further validate the effectiveness of each component in the overall architecture, confirming that the integration of state-space-based temporal sequence modeling and cross-domain feature fusion is critical for robust, high-quality speech enhancement. Our results also indicate that the proposed framework is well-suited for real-world applications where computational efficiency and superior performance are essential.