Spiking Neural Beamforming for Multichannel Speech Enhancement on the Edge

Abstract

Speech enhancement with wearable and edge devices requires architectures that balance enhancement quality against power and memory resources. Spiking Neural Networks (SNNs) offer a promising path towards this goal through sparse, event-driven computation, but existing SNN-based speech enhancement methods are largely restricted to single-channel processing and therefore cannot exploit the spatial diversity available from microphone arrays. In this work, we propose a fully SNN framework for multichannel speech enhancement based on a fully connected architecture (FC-SNN) with sequential spike encoding, derived from a compact artificial neural network (ANN) baseline via leaky integrate-and-fire neurons. We characterize how enhancement performance scales with model size and spike-train length relative to ANN and classical beamforming baselines, and report the resulting parameter counts and computational complexity. We further compare direct and rate spike encoding, revealing a clear trade-off between event-driven computation and enhancement performance, and use these results to identify favorable configurations for edge devices.