  Speech enhancement seeks to extract clean speech from noisy signals.
Traditional deep learning methods face two challenges: efficiently using
information in long speech sequences and high computational costs. To address
these, we introduce the Spiking Structured State Space Model (Spiking-S4). This
approach merges the energy efficiency of Spiking Neural Networks (SNN) with the
long-range sequence modeling capabilities of Structured State Space Models
(S4), offering a compelling solution. Evaluation on the DNS Challenge and
VoiceBank+Demand Datasets confirms that Spiking-S4 rivals existing Artificial
Neural Network (ANN) methods but with fewer computational resources, as
evidenced by reduced parameters and Floating Point Operations (FLOPs).

Sound;Computer Vision and Pattern Recognition;Audio and Speech Processing