Neuromorphic Speech Enhancement with Dual-Branch Spiking Neural Networks
Spiking neural network (SNN)-based neuromorphic speech enhancement has emerged as a promising paradigm due to its energy efficiency, yet it still underperforms classical artificial neural network (ANN)-based approaches owing to binary activations and the lack of well-designed network architectures. To overcome this limitation, we propose a novel dual-branch spiking neural network architecture equipped with a gated spiking unit (GSU), termed GSU-DBNet. Specifically, GSU-DBNet simultaneously models the speech magnitude spectrum and complex spectrum, predicting the corresponding magnitude and complex spectral masks. Meanwhile, a dual-path GSU module is adopted to exploit temporal and frequen...
| Authors | Taiyu Meng, Wenbin Jiang, Haoyi Zhang et al. |
|---|---|
| Primary category | cs.SD |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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