13 citations · 33 across the 42 of their papers we have counts for
4 papers · 2 filters
MARS-Sep: Multimodal-Aligned Reinforced Sound Separation
Zihan Zhang, Xize Cheng, Zhennan Jiang +4
Universal sound separation faces a fundamental misalignment: models optimized for low-level signal metrics often produce semantically contaminated outputs, failing to suppress perc…
T2A-Feedback: Improving Basic Capabilities of Text-to-Audio Generation via Fine-grained AI Feedback
Zehan Wang, Ke Lei, Chen Zhu +8
Text-to-audio (T2A) generation has achieved remarkable progress in generating a variety of audio outputs from language prompts. However, current state-of-the-art T2A models still s…
Unleashing the Power of Natural Audio Featuring Multiple Sound Sources
Xize Cheng, Slytherin Wang, Zehan Wang +3
Universal sound separation aims to extract clean audio tracks corresponding to distinct events from mixed audio, which is critical for artificial auditory perception. However, curr…
Enhancing Expressive Voice Conversion with Discrete Pitch-Conditioned Flow Matching Model
Jialong Zuo, Shengpeng Ji, Minghui Fang +8
This paper introduces PFlow-VC, a conditional flow matching voice conversion model that leverages fine-grained discrete pitch tokens and target speaker prompt information for expre…