1.6k citations · 2k across the 18 of their papers we have counts for
6 papers · 1 filter
Cycle-Consistent Speech Enhancement
Zhong Meng, Jinyu Li, Yifan Gong +2
Feature mapping using deep neural networks is an effective approach for single-channel speech enhancement. Noisy features are transformed to the enhanced ones through a mapping net…
Adversarial Feature-Mapping for Speech Enhancement
Zhong Meng, Jinyu Li, Yifan Gong +2
Feature-mapping with deep neural networks is commonly used for single-channel speech enhancement, in which a feature-mapping network directly transforms the noisy features to the c…
Channel Agnostic End-to-End Learning based Communication Systems with Conditional GAN
Hao Ye, Geoffrey Ye Li, Biing-Hwang Fred Juang +1
In this article, we use deep neural networks (DNNs) to develop a wireless end-to-end communication system, in which DNNs are employed for all signal-related functionalities, such a…
Deep Learning in Physical Layer Communications
Zhijin Qin, Hao Ye, Geoffrey Ye Li +1
Deep learning (DL) has shown the great potentials to break the bottleneck of communication systems. This article provides an overview on the recent advancements in DL-based physica…
Deep Reinforcement Learning based Resource Allocation for V2V Communications
Hao Ye, Geoffrey Ye Li, Biing-Hwang Fred Juang
In this paper, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communications based on deep reinforcement learning, which can be applied to bo…
Speaker-Invariant Training via Adversarial Learning
Zhong Meng, Jinyu Li, Zhuo Chen +5
We propose a novel adversarial multi-task learning scheme, aiming at actively curtailing the inter-talker feature variability while maximizing its senone discriminability so as to…