arXiv subjects

Yihang Bao

Publications and source records attributed to Yihang Bao.

3 recordsLinked to original sources

arXiv2026

Rel2Traj: Inter-Source Relation-Aware Trajectory Generation for Text-To-Spatial-Audio

"A car drives from front left to front right while a dog follows it." Such a description specifies no particular trajectory, yet imposes a constraint on the joint motion of both sources that must hold at every frame. Existing text-to-spatial-audio methods can position and move each sound source according to its description, but none of them ensures or verifies that such inter-source relations hold. We therefore propose Rel2Traj, which parses the description into sources, relations, and anchors, jointly generates the trajectories of all sources using a relation-conditioned diffusion model, and finally renders sounds from a pretrained text-to-audio model along these trajectories to produce binaural audio. For training and evaluation, we construct a synthetic dataset from a controlled language of source motions and inter-source relations. A single frame-wise relational geometry defines both our evaluation metric, the Relation Satisfaction Rate (RSR), and an auxiliary training loss. On motion-relation combinations held out from training, Rel2Traj maintains all relation types, whereas every baseline fails on at least one. A listening test further confirms that listeners recognize the described relations. To our knowledge, this is the first text-to-spatial-audio work to model relations among moving sources as explicit geometric constraints.

cs.SD↗

arXiv2026

The Vulnerability of Neural Audio Watermarks under Speech Enhancement

Neural audio watermarks are increasingly deployed in commercial speech generation systems to make AI-generated speech traceable, yet their robustness has been studied mainly under conventional signal distortions. Since a watermark can be regarded as imperceptible noise added to the speech signal, a natural question is whether speech enhancement (SE), as a denoising model, can remove it. In this paper, we cascade Gaussian noise with SE models as a black-box watermark removal attack, covering both discriminative and generative SE paradigms, against six neural watermarks: AudioSeal, WavMark, SilentCipher, Timbre, Perth, and AlignMark. Experimental results show that the proposed attack significantly outperforms existing neural re-synthesis methods in watermark removal. In particular, we find that generative SE, which reconstructs the harmonic regions of speech while denoising, is highly destructive to watermarks. These findings show that SE poses a serious threat to current audio watermarking methods, and we call for SE-aware robustness evaluation in watermark design.

cs.SD↗

arXiv2025

Submillimeter-Accurate 3D Lumbar Spine Reconstruction from Biplanar X-Ray Images: Incorporating a Multi-Task Network and Landmark-Weighted Loss

To meet the clinical demand for accurate 3D lumbar spine assessment in a weight-bearing position, this study presents a novel, fully automatic framework for high-precision 3D reconstruction from biplanar X-ray images, overcoming the limitations of existing methods. The core of this method involves a novel multi-task deep learning network that simultaneously performs lumbar decomposition and landmark detection on the original biplanar radiographs. The decomposition effectively eliminates interference from surrounding tissues, simplifying subsequent image registration, while the landmark detection provides an initial pose estimation for the Statistical Shape Model (SSM), enhancing the efficiency and robustness of the registration process. Building on this, we introduce a landmark-weighted 2D-3D registration strategy. By assigning higher weights to complex posterior structures like the transverse and spinous processes during optimization, this strategy significantly enhances the reconstruction accuracy of the posterior arch. Our method was validated against a gold standard derived from registering CT segmentations to the biplanar X-rays. It sets a new benchmark by achieving sub-millimeter accuracy and completes the full reconstruction and measurement workflow in under 20 seconds, establishing a state-of-the-art combination of precision and speed. This fast and low-dose pipeline provides a powerful automated tool for diagnosing lumbar conditions such as spondylolisthesis and scoliosis in their functional, weight-bearing state.

eess.IV↗