Spiking Structured State Space Model for Monaural Speech Enhancement
Yu Du Xu Liu Yansong Chua
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).