11 citations · 12 across the 5 of their papers we have counts for
5 papers
Rethinking Flow and Diffusion Bridge Models for Speech Enhancement
Dahan Wang, Jun Gao, Tong Lei +4
Flow matching and diffusion bridge models have emerged as leading paradigms in generative speech enhancement, modeling stochastic processes between paired noisy and clean speech si…
TS-URGENet: A Three-stage Universal Robust and Generalizable Speech Enhancement Network
Xiaobin Rong, Dahan Wang, Qinwen Hu +3
Universal speech enhancement aims to handle input speech with different distortions and input formats. To tackle this challenge, we present TS-URGENet, a Three-Stage Universal, Rob…
UL-UNAS: Ultra-Lightweight U-Nets for Real-Time Speech Enhancement via Network Architecture Search
Xiaobin Rong, Leyan Yang, Dahan Wang +4
Lightweight models are essential for real-time speech enhancement applications. In recent years, there has been a growing trend toward developing increasingly compact models for sp…
Adaptive Convolution for CNN-based Speech Enhancement Models
Dahan Wang, Xiaobin Rong, Shiruo Sun +3
Deep learning-based speech enhancement methods have significantly improved speech quality and intelligibility. Convolutional neural networks (CNNs) have been proven to be essential…
Semi-blind source separation using convolutive transfer function for nonlinear acoustic echo cancellation
Guoliang Cheng, Lele Liao, Kai Chen +3
The recently proposed semi-blind source separation (SBSS) method for nonlinear acoustic echo cancellation (NAEC) outperforms adaptive NAEC in attenuating the nonlinear acoustic ech…