  Speech enhancement (SE) is crucial for reliable communication devices or
robust speech recognition systems. Although conventional artificial neural
networks (ANN) have demonstrated remarkable performance in SE, they require
significant computational power, along with high energy costs. In this paper,
we propose a novel approach to SE using a spiking neural network (SNN) based on
a U-Net architecture. SNNs are suitable for processing data with a temporal
dimension, such as speech, and are known for their energy-efficient
implementation on neuromorphic hardware. As such, SNNs are thus interesting
candidates for real-time applications on devices with limited resources. The
primary objective of the current work is to develop an SNN-based model with
comparable performance to a state-of-the-art ANN model for SE. We train a deep
SNN using surrogate-gradient-based optimization and evaluate its performance
using perceptual objective tests under different signal-to-noise ratios and
real-world noise conditions. Our results demonstrate that the proposed
energy-efficient SNN model outperforms the Intel Neuromorphic Deep Noise
Suppression Challenge (Intel N-DNS Challenge) baseline solution and achieves
acceptable performance compared to an equivalent ANN model.
