Tingle Li

I am a first-year Ph.D. student at Berkeley Artificial Intelligence Research (BAIR) Lab, advised by Prof. Gopala Anumanchipalli.

Previously, I had the privilege to work with Prof. Hang Zhao from IIIS, Tsinghua University, Prof. Andrew Owens from The University of Michigan, and Prof. Ming Li from Duke University.

My research revolves around the intersections of audio signal processing and computer vision. I am particularly excited about studying training vision with audio texture and vice versa.


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lft Learning Visual Styles from Audio-Visual Associations
Tingle Li, Yichen Liu, Andrew Owens, Hang Zhao
ECCV, 2022  
PDF / Project Page / Code

We learn from unlabeled data to manipulate the style of an image using sound.

r2s Radio2Speech: High Quality Speech Recovery from Radio Frequency Signals
Running ZhaoJiangtao Yu, Tingle Li, Hang Zhao, Edith C.H. Ngai
Interspeech, 2022  
PDF / Project Page

High-quality speech recovery system for millimeter-wave radar without deafness.

umt Modality Laziness: Everybody's Business is Nobody's Business
Chenzhuang Du, Jiaye Teng, Tingle Li, Yichen Liu, Yue Wang, Yang Yuan, Hang Zhao
arXiv, 2022  

With multi-modal data as inputs, the encoders from naive fusion training will suffer from learning insufficient representations of each modality.

avd Neural Dubber: Dubbing for Videos According to Scripts
Chenxu Hu, Qiao Tian, Tingle Li, Yuping Wang, Yuxuan Wang, Hang Zhao
NeurIPS, 2021  
PDF / Project Page / Press

Automatic video dubbing driven by a neural network.

CVC CVC: Contrastive Learning for Non-parallel Voice Conversion
Tingle Li, Yichen Liu, Chenxu Hu, Hang Zhao
Interspeech, 2021 (ISCA Student Travel Grant) 
PDF / Project Page / Code

One-way GAN training for non-parallel voice conversion.

sams Sams-Net: A Sliced Attention-based Neural Network for Music Source Separation
Tingle Li, Jiawei Chen, Haowen Hou, Ming Li
ISCSLP, 2021 (Oral, Best Undergraduate Dissertation) 
PDF / Project Page

The scope of attention is narrowed down to the intra-chunk musical features that are most likely to affect each other.

fsc The DKU Speech Activity Detection and Speaker Identification Systems for Fearless Steps Challenge Phase-02
Qingjian Lin, Tingle Li, Ming Li
Interspeech, 2020  
PDF / Leaderboard

SoTA performance for speech activity detection and speaker identification.

atss Atss-Net: Target Speaker Separation via Attention-based Neural Network
Tingle Li, Qingjian Lin, Yuanyuan Bao, Ming Li
Interspeech, 2020  
PDF / Project Page

Adapted Transformer to the speech separation for more efficient and generalizable performance.

oml Optimal Mapping Loss: A Faster Loss for End-to-End Speaker Diarization
Qingjian Lin, Tingle Li, Lin Yang, Junjie Wang, Ming Li
Odyssey, 2020  

A new mapping loss based on Hungarian algorithm that reduces time complexity while maintaining performance for speaker diarization.

Last updated Oct. 2022.
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