Yudong He

Shek Lung Tsai Stone Forest

Hong Kong

Bio

I am currently a postdoctoral researcher at The Hong Kong University of Science and Technology (HKUST). I reveived a BSc degree (2017) in Applied Physics from the University of Science and Technology of China (USTC), and received a PhD degree (2022) in Industrial Engineering and Decision Analytics (IEDA) from HKUST.

Research interests

Audio signal processing, including but not limited to separation (also known as the cocktail party problem), denoising, localization, recognition;

Statistical signal processing, especially involving sparsity pursuing, compressive sensing, and sparse representation;

Fuzzy clustering, especially for unequal size classes (imbalanced data)

Contact

Email: yudong@ust.hk

news

Nov 27, 2025	Our paper “A Novel Property to Modify Weighted l1 Minimization for Improved Compressed Sensing” has been accepted for publication in Signal Processing, 2024 - Elsevier!
Jan 21, 2025	I will give a presentation at ICASSP 2025. See you in India!
Dec 21, 2024	Our paper “A Novel Weighted Sparse Component Analysis for Underdetermined Blind Speech Separation” has been accepted for presentation at ICASSP 2025!

selected publications

ICASSP

A Novel Weighted Sparse Component Analysis for Underdetermined Blind Source Separation

Yudong He, Baeck Hyun Woo, and Richard H.Y. So

2025

Bib

@article{HE2025ICASSP,
  title = {A Novel Weighted Sparse Component Analysis for Underdetermined Blind Source Separation},
  year = {2025},
  author = {He, Yudong and Woo, Baeck Hyun and So, Richard H.Y.},
  keywords = {blind source separation, underdetermined linear system, sparse component analysis, weighted l1 minimization},
  organization = {IEEE},
}

J. SP
A novel property to modify weighted l1 minimization for improved compressed sensing

Yudong He, Baeck Hyun Woo, Fauzan Abdurrahim, and 1 more author

2024

Abs DOI Bib PDF

Weighted l1 minimization schemes are common methods to achieve compressed sensing (CS). However, they fail in the presence of inaccurate prior knowledge or improper scaling of weights due to inappropriately assigned large weights causing large and destructive errors in signal recovery. This paper proposes a theory-based algorithm to identify and correct such destructive weights for each signal entry. The enhancement is achieved through a novel sparsity-inducing property (SIP) which establishes a necessary condition for successful signal recovery. SIP outperforms existing properties such as coherence, restricted isometry property, and nullspace property by indicating which signal entries fail to be recovered. This unique advantage enables us to correct destructive weights that do not satisfy the SIP condition, making signal recovery successful where it previously failed. Results from many numerical experiments demonstrate that our proposed method can improve the signal recovery capability, robustness, and stability of the weighted l1 minimization for a wide range of applications, including sparse and compressive signal recovery, noise-aware recovery, sparse error correction, fast image acquisition, and sub-Nyquist sampling.
@article{HE2024109828, title = {A novel property to modify weighted l1 minimization for improved compressed sensing}, booktitle = {Signal Processing}, pages = {109828}, year = {2024}, issn = {0165-1684}, doi = {https://doi.org/10.1016/j.sigpro.2024.109828}, url = {https://www.sciencedirect.com/science/article/pii/S0165168424004481}, author = {He, Yudong and Woo, Baeck Hyun and Abdurrahim, Fauzan and So, Richard H.Y.}, keywords = {Sparsity, Sparsity-inducing property, Weighted minimization, Compressed sensing, Underdetermined linear system}, organization = {Elsevier}, }
ICASSP
Harvesting Partially-Disjoint Time-Frequency Information for Improving Degenerate Unmixing Estimation Technique

Yudong He, He Wang, Qifeng Chen, and 1 more author

In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022

Abs Bib PDF

The degenerate unmixing estimation technique (DUET) is one of the most efficient blind source separation algorithms tackling the challenging situation when the number of sources exceeds the number of microphones. However, as a time-frequency mask-based method, DUET erroneously results in interference components retention when source signals overlap each other in both frequency and time domains. In this paper, to avoid the erroneous retention, instead of masking, we propose to use multiple linear spatial filters (e.g., the minimum variance distortionless response filter) to extract the desired signals. These filters are constructed based on the information embedded in the detected single-source-points, that is, time-frequency points contributed by a single source. In comparison with the conventional DUET, our method achieved an impressive improvement greater than 5 dB in the source-to-interference ratio and 2 to 5 dB improvement in the source-to-distortion ratio, respectively. Findings are substantiated by unmixing simulation using live-recorded mixture signals from up to four sources. Audio examples can be found on the web page: "https://ydcnanhe.github.io/demo-icassp2022/"
@inproceedings{he2022harvesting, title = {Harvesting Partially-Disjoint Time-Frequency Information for Improving Degenerate Unmixing Estimation Technique}, author = {He, Yudong and Wang, He and Chen, Qifeng and So, Richard HY}, booktitle = {ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages = {506--510}, year = {2022}, organization = {IEEE}, url = {https://ieeexplore.ieee.org/abstract/document/9747679}, }