SSE-Net: Toward Low-Power-Consumption Spiking Neural Network for Monaural Speech Enhancement

Speech enhancement (SE) as a front-end task, especially in edge device deployment, requires low complexity and power consumption. However, deploying most artificial neural network (ANN)-based SE models while ensuring performance is challenging, particularly with the rapid development of large models in recent years. Spiking neural networks (SNNs) have shown potential in reducing power consumption. However, SNN-based SE models face two main challenges: on one hand, directly converting an ANN model to a SNN model introduces redundant modules, leading to difficulties in model training and information mismatch; on the other hand, the discrete binary activation and complex spatio-temporal dynamics of SNNs often result in information loss. To address these challenges, this paper proposes the SNN Speech Enhancement Network (SSE-Net). Unlike conventional approaches that convert ANNs to SNNs, all modules in SSE-Net are specifically designed for spike signal characteristics. Furthermore, the method innovatively develops a spiking feature extraction group that simultaneously converts information into spike signals while extracting original speech features, thereby effectively reducing information loss while significantly lowering both power consumption and computational complexity. The proposed method further designs information transformation blocks to convert spike signals back into continuous signals, fine-tuning and supplementing the speech information. Experimental results demonstrate that our proposed model achieves state of the art (SOTA) performance compared to the best SNN-SE models, with a 62% reduction in power proxy and 11% reduction in power cost, it also demonstrates strong competitiveness when compared with ANN-based SE models.