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Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Existing subnetwork-based distributed speech enhancement (DSE) algorithms merely consider single target source scenarios and do not exploit inter-subnetwork information exchange in multi-source cases,

Section: Publications & Resources

Publication: IEEE Transactions on Image Processing

Skeleton-based action recognition has gained increasing attention due to its robustness to viewpoint variations and background clutter. However, existing approaches still face challenges in feature mo

Section: Publications & Resources

Publication: IEEE Transactions on Audio, Speech and Language Processing

The safety alignment of large language models (LLMs) faces persistent challenges from jailbreak attacks. While existing methods mostly leverage prompt engineering or adversarial optimization, we ident

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Lipschitz regularization controls how strongly a denoising autoencoder propagates an input perturbation to its output, and has been used for photoplethysmography (PPG) denoising. We revisit the gradie

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Pillow-based ballistocardiography (BCG) enables unobtrusive cardiac monitoring, but J-peak detection is commonly learned as dense sequence labeling although the desired output is a sparse event set. T

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

This letter proposes LocMark, a local feature modulation-based conditional watermarking method, to address the limitation that existing watermarking methods for deepfake proactive forensics are vulner

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

This paper investigates the optimal sensor configuration problem for bearings-only target localization. A unified analytical model for localization accuracy is established, applicable to both 2D and 3

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Most deep learning–based sound source localization methods rely on specific microphone array geometries, which require retraining for different configurations and lead to high cost. IPDnet is our prev

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

  Maximum-likelihood expectation-maximization (MLEM) is a widely used statistical reconstruction method in computed tomography (CT), but under ill-posed acquisition it progressively amplifies high-fre

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Variance stabilization with the generalized Anscombe transform (GAT) enables frozen Gaussian denoisers to process Poisson–Gaussian (PG) RAW noise, but its reliability depends on fitted shot/read-noise