Search Results for author: Zhen Ye

Found 7 papers, 3 papers with code

FlashSpeech: Efficient Zero-Shot Speech Synthesis

1 code implementation23 Apr 2024 Zhen Ye, Zeqian Ju, Haohe Liu, Xu Tan, Jianyi Chen, Yiwen Lu, Peiwen Sun, Jiahao Pan, Weizhen Bian, Shulin He, Qifeng Liu, Yike Guo, Wei Xue

The generation processes of FlashSpeech can be achieved efficiently with one or two sampling steps while maintaining high audio quality and high similarity to the audio prompt for zero-shot speech generation.

Speech Synthesis Voice Conversion

CoMoSVC: Consistency Model-based Singing Voice Conversion

no code implementations3 Jan 2024 Yiwen Lu, Zhen Ye, Wei Xue, Xu Tan, Qifeng Liu, Yike Guo

The diffusion-based Singing Voice Conversion (SVC) methods have achieved remarkable performances, producing natural audios with high similarity to the target timbre.

Voice Conversion

NAS-FM: Neural Architecture Search for Tunable and Interpretable Sound Synthesis based on Frequency Modulation

no code implementations22 May 2023 Zhen Ye, Wei Xue, Xu Tan, Qifeng Liu, Yike Guo

Since expert knowledge is hard to acquire, it hinders the flexibility to quickly design and tune digital synthesizers for diverse sounds.

Neural Architecture Search

CoMoSpeech: One-Step Speech and Singing Voice Synthesis via Consistency Model

1 code implementation11 May 2023 Zhen Ye, Wei Xue, Xu Tan, Jie Chen, Qifeng Liu, Yike Guo

In this paper, we propose a "Co"nsistency "Mo"del-based "Speech" synthesis method, CoMoSpeech, which achieve speech synthesis through a single diffusion sampling step while achieving high audio quality.

Denoising Singing Voice Synthesis +1

Pairwise Point Cloud Registration using Graph Matching and Rotation-invariant Features

no code implementations5 May 2021 Rong Huang, Wei Yao, Yusheng Xu, Zhen Ye, Uwe Stilla

Registration is a fundamental but critical task in point cloud processing, which usually depends on finding element correspondence from two point clouds.

Graph Matching Point Cloud Registration +1

BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving

1 code implementation15 Mar 2019 Jianru Xue, Jianwu Fang, Tao Li, Bohua Zhang, Pu Zhang, Zhen Ye, Jian Dou

Instead, BLVD aims to provide a platform for the tasks of dynamic 4D (3D+temporal) tracking, 5D (4D+interactive) interactive event recognition and intention prediction.

Autonomous Driving Instance Segmentation +5

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