  In speech separation, both CNN- and Transformer-based models have
demonstrated robust separation capabilities, garnering significant attention
within the research community. However, CNN-based methods have limited
modelling capability for long-sequence audio, leading to suboptimal separation
performance. Conversely, Transformer-based methods are limited in practical
applications due to their high computational complexity. Notably, within
computer vision, Mamba-based methods have been celebrated for their formidable
performance and reduced computational requirements. In this paper, we propose a
network architecture for speech separation using a state-space model, namely
SPMamba. We adopt the TF-GridNet model as the foundational framework and
substitute its Transformer component with a bidirectional Mamba module, aiming
to capture a broader range of contextual information. Our experimental results
reveal an important role in the performance aspects of Mamba-based models.
SPMamba demonstrates superior performance with a significant advantage over
existing separation models in a dataset built on Librispeech. Notably, SPMamba
achieves a substantial improvement in separation quality, with a 2.42 dB
enhancement in SI-SNRi compared to the TF-GridNet. The source code for SPMamba
is publicly accessible at https://github.com/JusperLee/SPMamba .
