1 code implementation • 4 Nov 2022 • Chen-Chou Lo, Patrick Vandewalle
Instead of using readout tokens, radar representations contribute additional depth information to a monocular depth estimation model and improve performance.
no code implementations • 26 Feb 2022 • Chen-Chou Lo, Patrick Vandewalle
In the supervision experiment, a monocular depth estimation model is trained under radar supervision to show the intrinsic depth information that radar can contribute.
1 code implementation • 15 Jul 2021 • Chen-Chou Lo, Patrick Vandewalle
We integrate sparse radar data into a monocular depth estimation model and introduce a novel preprocessing method for reducing the sparseness and limited field of view provided by radar.
1 code implementation • 24 May 2020 • Shang-Yi Chuang, Yu Tsao, Chen-Chou Lo, Hsin-Min Wang
Previous studies have confirmed the effectiveness of incorporating visual information into speech enhancement (SE) systems.
1 code implementation • 22 Jan 2020 • Wen-Chin Huang, Hao Luo, Hsin-Te Hwang, Chen-Chou Lo, Yu-Huai Peng, Yu Tsao, Hsin-Min Wang
In this paper, we extend the CDVAE-VC framework by incorporating the concept of adversarial learning, in order to further increase the degree of disentanglement, thereby improving the quality and similarity of converted speech.
1 code implementation • 2 May 2019 • Wen-Chin Huang, Yi-Chiao Wu, Chen-Chou Lo, Patrick Lumban Tobing, Tomoki Hayashi, Kazuhiro Kobayashi, Tomoki Toda, Yu Tsao, Hsin-Min Wang
Such hypothesis implies that during the conversion phase, the latent codes and the converted features in VAE based VC are in fact source F0 dependent.
6 code implementations • 17 Apr 2019 • Chen-Chou Lo, Szu-Wei Fu, Wen-Chin Huang, Xin Wang, Junichi Yamagishi, Yu Tsao, Hsin-Min Wang
In this paper, we propose deep learning-based assessment models to predict human ratings of converted speech.