5 papers
FSC-Net: Integrating Fast Fourier Convolutions and Progressive Learning for Speech Bandwidth Extension
Xinan Chen, Xiaobin Rong, Qinwen Hu +2
Speech bandwidth extension (BWE) aims to reconstruct high-fidelity wideband audio from narrowband inputs. While recent approaches have made significant progress, they often struggl…
PASE: Leveraging the Phonological Prior of WavLM for Low-Hallucination Generative Speech Enhancement
Xiaobin Rong, Qinwen Hu, Mansur Yesilbursa +2
Generative models have shown remarkable performance in speech enhancement (SE), achieving superior perceptual quality over traditional discriminative approaches. However, existing…
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…
FNSE-SBGAN: Far-field Speech Enhancement with Schrodinger Bridge and Generative Adversarial Networks
Tong Lei, Qinwen Hu, Ziyao Lin +5
The prevailing method for neural speech enhancement predominantly utilizes fully-supervised deep learning with simulated pairs of far-field noisy-reverberant speech and clean speec…
SNR-Progressive Model with Harmonic Compensation for Low-SNR Speech Enhancement
Zhongshu Hou, Tong Lei, Qinwen Hu +3
Despite significant progress made in the last decade, deep neural network (DNN) based speech enhancement (SE) still faces the challenge of notable degradation in the quality of rec…