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cs.SD2026
The Vulnerability of Neural Audio Watermarks under Speech Enhancement
Xincong Zhong, Shengyao Wang, Lingfeng Yao +4
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…
cs.SD2026
PhysWave: Physics-Guided Latent Diffusion Models for Controllable Spatial Audio Generation
Lingfeng Yao, Chenpei Huang, Xingke Yang +5
Text-to-spatial audio generation, such as text-to-First-Order Ambisonics (FOA), provides a convenient way to create spatial audio for billion-dollar gaming and film industries. How…
cs.SD2025
EchoMark: Perceptual Acoustic Environment Transfer with Watermark-Embedded Room Impulse Response
Chenpei Huang, Lingfeng Yao, Kyu In Lee +3
Acoustic Environment Matching (AEM) is the task of transferring clean audio into a target acoustic environment, enabling engaging applications such as audio dubbing and auditory im…