262 citations · 295 across the 8 of their papers we have counts for
8 papers
Instance-Guided Radar Depth Estimation for 3D Object Detection
Chen-Chou Lo, Patrick Vandewalle
Accurate depth estimation is fundamental to 3D perception in autonomous driving, supporting tasks such as detection, tracking, and motion planning. However, monocular camera-based…
RCDPT: Radar-Camera fusion Dense Prediction Transformer
Chen-Chou Lo, Patrick Vandewalle
Recently, transformer networks have outperformed traditional deep neural networks in natural language processing and show a large potential in many computer vision tasks compared t…
How Much Depth Information can Radar Contribute to a Depth Estimation Model?
Chen-Chou Lo, Patrick Vandewalle
Recently, several works have proposed fusing radar data as an additional perceptual signal into monocular depth estimation models because radar data is robust against varying light…
Depth Estimation from Monocular Images and Sparse radar using Deep Ordinal Regression Network
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…
Lite Audio-Visual Speech Enhancement
Shang-Yi Chuang, Yu Tsao, Chen-Chou Lo +1
Previous studies have confirmed the effectiveness of incorporating visual information into speech enhancement (SE) systems. Despite improved denoising performance, two problems may…
Unsupervised Representation Disentanglement using Cross Domain Features and Adversarial Learning in Variational Autoencoder based Voice Conversion
Wen-Chin Huang, Hao Luo, Hsin-Te Hwang +4
An effective approach for voice conversion (VC) is to disentangle linguistic content from other components in the speech signal. The effectiveness of variational autoencoder (VAE)…
Investigation of F0 conditioning and Fully Convolutional Networks in Variational Autoencoder based Voice Conversion
Wen-Chin Huang, Yi-Chiao Wu, Chen-Chou Lo +6
In this work, we investigate the effectiveness of two techniques for improving variational autoencoder (VAE) based voice conversion (VC). First, we reconsider the relationship betw…
MOSNet: Deep Learning based Objective Assessment for Voice Conversion
Chen-Chou Lo, Szu-Wei Fu, Wen-Chin Huang +4
Existing objective evaluation metrics for voice conversion (VC) are not always correlated with human perception. Therefore, training VC models with such criteria may not effectivel…