9 papers
Global Rotation Equivariant Phase Modeling for Speech Enhancement with Deep Magnitude-Phase Interaction
Chengzhong Wang, Andong Li, Dingding Yao +1
While deep learning has advanced speech enhancement (SE), effective phase modeling remains challenging, as conventional networks typically operate within a flat Euclidean feature s…
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…
Rethinking the joint estimation of magnitude and phase for time-frequency domain neural vocoders
Lingling Dai, Andong Li, Tong Lei +3
Time-frequency (T-F) domain-based neural vocoders have shown promising results in synthesizing high-fidelity audio. Nevertheless, it remains unclear on the mechanism of effectively…
Spiking Vocos: An Energy-Efficient Neural Vocoder
Yukun Chen, Zhaoxi Mu, Andong Li +2
Despite the remarkable progress in the synthesis speed and fidelity of neural vocoders, their high energy consumption remains a critical barrier to practical deployment on computat…
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 c…
Advances in Speech Separation: Techniques, Challenges, and Future Trends
Kai Li, Guo Chen, Wendi Sang +8
The field of speech separation, addressing the "cocktail party problem", has seen revolutionary advances with DNNs. Speech separation enhances clarity in complex acoustic environme…