Search Results for author:
Found 8 papers, 6 papers with code
MOSNet: Deep Learning based Objective Assessment for Voice Conversion
In this paper, we propose deep learning-based assessment models to predict human ratings of converted speech.
Lite Audio-Visual Speech Enhancement
Previous studies have confirmed the effectiveness of incorporating visual information into speech enhancement (SE) systems.
Investigation of F0 conditioning and Fully Convolutional Networks in Variational Autoencoder based Voice Conversion
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.
Unsupervised Representation Disentanglement using Cross Domain Features and Adversarial Learning in Variational Autoencoder based Voice Conversion
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.
Depth Estimation from Monocular Images and Sparse radar using Deep Ordinal Regression Network
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.
RCDPT: Radar-Camera fusion Dense Prediction Transformer
Instead of using readout tokens, radar representations contribute additional depth information to a monocular depth estimation model and improve performance.
How Much Depth Information can Radar Contribute to a Depth Estimation Model?
In the supervision experiment, a monocular depth estimation model is trained under radar supervision to show the intrinsic depth information that radar can contribute.
