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Found 6 papers, 1 papers with code
Time-Variance Aware Real-Time Speech Enhancement
Time-variant factors often occur in real-world full-duplex communication applications.
Real-time speech enhancement with dynamic attention span
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.
Scale-Semantic Joint Decoupling Network for Image-text Retrieval in Remote Sensing
To address these issues, we propose a novel Scale-Semantic Joint Decoupling Network (SSJDN) for remote sensing image-text retrieval.
End-to-End Neural Speech Coding for Real-Time Communications
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).
Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization
Bayesian neural networks have been widely used in many applications because of the distinctive probabilistic representation framework.
Interactive Speech and Noise Modeling for Speech Enhancement
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)
