16 citations · 34 across the 9 of their papers we have counts for
9 papers
MeloTrans: A Text to Symbolic Music Generation Model Following Human Composition Habit
Yutian Wang, Wanyin Yang, Zhenrong Dai +3
At present, neural network models show powerful sequence prediction ability and are used in many automatic composition models. In comparison, the way humans compose music is very d…
Atomic-scale Deformation Process of Glasses Unveiled by Stress-induced Structural Anisotropy
Jie Dong, Hailong Peng, Hui Wang +7
Experimentally resolving atomic-scale structural changes of a deformed glass remains challenging owing to the disordered nature of glass structure. Here, we show that the structura…
Unsupervised Quantized Prosody Representation for Controllable Speech Synthesis
Yutian Wang, Yuankun Xie, Kun Zhao +2
In this paper, we propose a novel prosody disentangle method for prosodic Text-to-Speech (TTS) model, which introduces the vector quantization (VQ) method to the auxiliary prosody…
Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement
Guochen Yu, Andong Li, Wenzhe Liu +3
Due to the high computational complexity to model more frequency bands, it is still intractable to conduct real-time full-band speech enhancement based on deep neural networks. Rec…
DBT-Net: Dual-branch federative magnitude and phase estimation with attention-in-attention transformer for monaural speech enhancement
Guochen Yu, Andong Li, Hui Wang +3
The decoupling-style concept begins to ignite in the speech enhancement area, which decouples the original complex spectrum estimation task into multiple easier sub-tasks i.e., mag…
Dual-branch Attention-In-Attention Transformer for single-channel speech enhancement
Guochen Yu, Andong Li, Chengshi Zheng +3
Curriculum learning begins to thrive in the speech enhancement area, which decouples the original spectrum estimation task into multiple easier sub-tasks to achieve better performa…
A Two-stage Complex Network using Cycle-consistent Generative Adversarial Networks for Speech Enhancement
Guochen Yu, Yutian Wang, Hui Wang +2
Cycle-consistent generative adversarial networks (CycleGAN) have shown their promising performance for speech enhancement (SE), while one intractable shortcoming of these CycleGAN-…
Joint magnitude estimation and phase recovery using Cycle-in-Cycle GAN for non-parallel speech enhancement
Guochen Yu, Andong Li, Yutian Wang +3
For the lack of adequate paired noisy-clean speech corpus in many real scenarios, non-parallel training is a promising task for DNN-based speech enhancement methods. However, becau…
CycleGAN-based Non-parallel Speech Enhancement with an Adaptive Attention-in-attention Mechanism
Guochen Yu, Yutian Wang, Chengshi Zheng +2
Non-parallel training is a difficult but essential task for DNN-based speech enhancement methods, for the lack of adequate noisy and paired clean speech corpus in many real scenari…