28 citations · 61 across the 8 of their papers we have counts for
8 papers
VarietySound: Timbre-Controllable Video to Sound Generation via Unsupervised Information Disentanglement
Chenye Cui, Yi Ren, Jinglin Liu +2
Video to sound generation aims to generate realistic and natural sound given a video input. However, previous video-to-sound generation methods can only generate a random or averag…
SyntaSpeech: Syntax-Aware Generative Adversarial Text-to-Speech
Zhenhui Ye, Zhou Zhao, Yi Ren +1
The recent progress in non-autoregressive text-to-speech (NAR-TTS) has made fast and high-quality speech synthesis possible. However, current NAR-TTS models usually use phoneme seq…
FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis
Rongjie Huang, Max W. Y. Lam, Jun Wang +4
Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hinde…
Contrastive Learning with Positive-Negative Frame Mask for Music Representation
Dong Yao, Zhou Zhao, Shengyu Zhang +4
Self-supervised learning, especially contrastive learning, has made an outstanding contribution to the development of many deep learning research fields. Recently, researchers in t…
Learning the Beauty in Songs: Neural Singing Voice Beautifier
Jinglin Liu, Chengxi Li, Yi Ren +2
We are interested in a novel task, singing voice beautifying (SVB). Given the singing voice of an amateur singer, SVB aims to improve the intonation and vocal tone of the voice, wh…
Revisiting Over-Smoothness in Text to Speech
Yi Ren, Xu Tan, Tao Qin +2
Non-autoregressive text to speech (NAR-TTS) models have attracted much attention from both academia and industry due to their fast generation speed. One limitation of NAR-TTS model…