Neuromorphic Speech Enhancement with Dual-Branch Spiking Neural Networks

The article introduces GSU-DBNet, a dual-branch spiking neural network architecture designed for neuromorphic speech enhancement, featuring a gated spiking unit (GSU). This model simultaneously processes the speech magnitude and complex spectra, achieving a PESQ score of 3.04 with just 394K parameters, which is significantly fewer than traditional ANN models (4.5%–10.6% of their parameters). This advancement in SNN architecture enhances energy efficiency and spatiotemporal feature representation, making it a relevant development for practitioners focused on efficient AI speech processing solutions.