25 citations · 46 across the 10 of their papers we have counts for
10 papers
Self-Supervised Audio-Visual Soundscape Stylization
Tingle Li, Renhao Wang, Po-Yao Huang +2
Speech sounds convey a great deal of information about the scenes, resulting in a variety of effects ranging from reverberation to additional ambient sounds. In this paper, we mani…
Deep Speech Synthesis from MRI-Based Articulatory Representations
Peter Wu, Tingle Li, Yijing Lu +6
In this paper, we study articulatory synthesis, a speech synthesis method using human vocal tract information that offers a way to develop efficient, generalizable and interpretabl…
On Uni-Modal Feature Learning in Supervised Multi-Modal Learning
Chenzhuang Du, Jiaye Teng, Tingle Li +5
We abstract the features (i.e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which…
Radio2Speech: High Quality Speech Recovery from Radio Frequency Signals
Running Zhao, Jiangtao Yu, Tingle Li +2
Considering the microphone is easily affected by noise and soundproof materials, the radio frequency (RF) signal is a promising candidate to recover audio as it is immune to noise…
Learning Visual Styles from Audio-Visual Associations
Tingle Li, Yichen Liu, Andrew Owens +1
From the patter of rain to the crunch of snow, the sounds we hear often convey the visual textures that appear within a scene. In this paper, we present a method for learning visua…
Neural Dubber: Dubbing for Videos According to Scripts
Chenxu Hu, Qiao Tian, Tingle Li +3
Dubbing is a post-production process of re-recording actors' dialogues, which is extensively used in filmmaking and video production. It is usually performed manually by profession…
Improving Multi-Modal Learning with Uni-Modal Teachers
Chenzhuang Du, Tingle Li, Yichen Liu +4
Learning multi-modal representations is an essential step towards real-world robotic applications, and various multi-modal fusion models have been developed for this purpose. Howev…
CVC: Contrastive Learning for Non-parallel Voice Conversion
Tingle Li, Yichen Liu, Chenxu Hu +1
Cycle consistent generative adversarial network (CycleGAN) and variational autoencoder (VAE) based models have gained popularity in non-parallel voice conversion recently. However,…
Atss-Net: Target Speaker Separation via Attention-based Neural Network
Tingle Li, Qingjian Lin, Yuanyuan Bao +1
Recently, Convolutional Neural Network (CNN) and Long short-term memory (LSTM) based models have been introduced to deep learning-based target speaker separation. In this paper, we…
Sams-Net: A Sliced Attention-based Neural Network for Music Source Separation
Tingle Li, Jiawei Chen, Haowen Hou +1
Convolutional Neural Network (CNN) or Long short-term memory (LSTM) based models with the input of spectrogram or waveforms are commonly used for deep learning based audio source s…