no code implementations • 25 Feb 2023 • Chengyu Zheng, Yuan Zhou, Xiulian Peng, Yuan Zhang, Yan Lu
Time-variant factors often occur in real-world full-duplex communication applications.
no code implementations • 21 Feb 2023 • Chengyu Zheng, Yuan Zhou, Xiulian Peng, Yuan Zhang, Yan Lu
For real-time speech enhancement (SE) including noise suppression, dereverberation and acoustic echo cancellation, the time-variance of the audio signals becomes a severe challenge.
no code implementations • 12 Dec 2022 • Chengyu Zheng, Ning Song, Ruoyu Zhang, Lei Huang, Zhiqiang Wei, Jie Nie
To address these issues, we propose a novel Scale-Semantic Joint Decoupling Network (SSJDN) for remote sensing image-text retrieval.
no code implementations • 24 Jan 2022 • Xue Jiang, Xiulian Peng, Chengyu Zheng, Huaying Xue, Yuan Zhang, Yan Lu
Deep-learning based methods have shown their advantages in audio coding over traditional ones but limited attention has been paid on real-time communications (RTC).
1 code implementation • CVPR 2021 • Jiaru Zhang, Yang Hua, Zhengui Xue, Tao Song, Chengyu Zheng, Ruhui Ma, Haibing Guan
Bayesian neural networks have been widely used in many applications because of the distinctive probabilistic representation framework.
no code implementations • 17 Dec 2020 • Chengyu Zheng, Xiulian Peng, Yuan Zhang, Sriram Srinivasan, Yan Lu
In this paper, we propose a novel idea to model speech and noise simultaneously in a two-branch convolutional neural network, namely SN-Net.
Ranked #1 on Speech Enhancement on Deep Noise Suppression (DNS) Challenge (PESQ-NB metric)