6 papers · 1 filter
Scalable Neural Vocoder from Range-Null Space Decomposition
Andong Li, Tong Lei, Zhihang Sun +4
Although deep neural networks have facilitated significant progress of neural vocoders in recent years, they usually suffer from intrinsic challenges like opaque modeling, inflexib…
BridgeVoC: Revitalizing Neural Vocoder from a Restoration Perspective
Andong Li, Tong Lei, Rilin Chen +5
This paper revisits the neural vocoder task through the lens of audio restoration and propose a novel diffusion vocoder called BridgeVoC. Specifically, by rank analysis, we compare…
Target matching based generative model for speech enhancement
Taihui Wang, Rilin Chen, Tong Lei +4
The design of mean and variance schedules for the perturbed signal is a fundamental challenge in generative models. While score-based and Schrödinger bridge-based models require ca…
Audio-Thinker: Guiding Audio Language Model When and How to Think via Reinforcement Learning
Shu Wu, Chenxing Li, Wenfu Wang +4
Recent advancements in large language models, multimodal large language models, and large audio language models (LALMs) have significantly improved their reasoning capabilities thr…
From Continuous to Discrete: Cross-Domain Collaborative General Speech Enhancement via Hierarchical Language Models
Zhaoxi Mu, Rilin Chen, Andong Li +3
This paper introduces OmniGSE, a novel general speech enhancement (GSE) framework designed to mitigate the diverse distortions that speech signals encounter in real-world scenarios…
SMRU: Split-and-Merge Recurrent-based UNet for Acoustic Echo Cancellation and Noise Suppression
Zhihang Sun, Andong Li, Rilin Chen +4
The proliferation of deep neural networks has spawned the rapid development of acoustic echo cancellation and noise suppression, and plenty of prior arts have been proposed, which…