IIFC-Net: A Monaural Speech Enhancement Network With High-Order Information Interaction and Feature Calibration
Wenbing Wei, Ying Hu, Hao Huang, Liang He
Abstract
Recently, many Transformer-style dual-path models have achieved impressive performance for speech enhancement. However, their high parameters and computational complexity hinder their practical application. In this letter, we propose a monaural speech enhancement network with lower parameter count and complexity based on high-order information interaction and feature calibration (IIFC-Net). The network includes high-order information interaction Transformer (HOIIFormer) with high-order information interaction (HOII) block instead of a multi-head self-attention (MHSA) in Transformer. IIFC-Net leverages dual-path HOIIFormer (DPH) to model the distant dependency relation along time and frequency dimensions, respectively, and effectively captures deep-level information through the HOII block. We also design a feature calibration (FC) block to enhance the frequency components of target speech, which can be verified by a visualization analysis. The outcomes of experiments conducted on the VoiceBank+DEMAND and WHAMR! Datasets demonstrate that IIFC-Net achieves comparable performance in terms of denoising, dereverberation, and simultaneous denoising & dereverberation.