Bridging Quality and Efficiency With Spiking Mamba for Speech Bandwidth Extension

Abstract

Artificial neural networks (ANN) have significantly advanced speech bandwidth extension (BWE) but suffer from high computational complexity, limiting deployment on power-constrained edge devices. While spiking neural networks (SNNs) offer an energy-efficient alternative, they often struggle to capture the long-range temporal dependencies required for high-fidelity reconstruction. To address this, we propose SpikeBWE+, a novel framework that synergizes SNNs efficiency with the global modeling capabilities of the Mamba architecture. Central to our approach is the mamba spiking neuron (MSN), which integrates a dual-path Selective State Space Model into spiking architecture to process local and global contexts simultaneously. We further introduce a residual Spiking Connection (RSC) that injects global features directly into the spiking unit to effectively modulate threshold dynamics and optimize sparsity. Experimental evaluations on the TIMIT and VCTK datasets demonstrate that SpikeBWE+ achieves superior reconstruction quality (LSD of 1.14) comparable to complex ANN baselines while significantly reducing energy consumption (energy cost of 1.24 mJ), establishing a new benchmark for BWE.