20 citations · 22 across the 10 of their papers we have counts for
11 papers
SleuthVLM: Do We Really Need Audio-in-Video for Omni Video Understanding?
Peiran Wu, Fangfei Gou, Zhaoxi Mu +6
Omni models answer questions about videos by jointly processing sampled frames and audio in one turn, with progress driven largely by scaling model size and omni training data. We…
VocalRender: Score-Native Singing Voice Synthesis for Real-World Composition
Yukun Chen, Tianrui Wang, Zhaoxi Mu +2
Existing singing voice synthesis systems often require predefined durations, explicit duration prediction, or time-aligned acoustic guidance, which limits their compatibility with…
VocalParse: Towards Unified and Scalable Singing Voice Transcription with Large Audio Language Models
Yukun Chen, Tianrui Wang, Zhaoxi Mu +2
High-quality singing annotations are fundamental to modern Singing Voice Synthesis (SVS) systems. However, obtaining these annotations at scale through manual labeling is unrealist…
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…
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…
SepALM: Audio Language Models Are Error Correctors for Robust Speech Separation
Zhaoxi Mu, Xinyu Yang, Gang Wang
While contemporary speech separation technologies adeptly process lengthy mixed audio waveforms, they are frequently challenged by the intricacies of real-world environments, inclu…
Separate in the Speech Chain: Cross-Modal Conditional Audio-Visual Target Speech Extraction
Zhaoxi Mu, Xinyu Yang
The integration of visual cues has revitalized the performance of the target speech extraction task, elevating it to the forefront of the field. Nevertheless, this multi-modal lear…
Self-Supervised Disentangled Representation Learning for Robust Target Speech Extraction
Zhaoxi Mu, Xinyu Yang, Sining Sun +1
Speech signals are inherently complex as they encompass both global acoustic characteristics and local semantic information. However, in the task of target speech extraction, certa…
Multi-Dimensional and Multi-Scale Modeling for Speech Separation Optimized by Discriminative Learning
Zhaoxi Mu, Xinyu Yang, Wenjing Zhu
Transformer has shown advanced performance in speech separation, benefiting from its ability to capture global features. However, capturing local features and channel information o…
A Multi-Stage Triple-Path Method for Speech Separation in Noisy and Reverberant Environments
Zhaoxi Mu, Xinyu Yang, Xiangyuan Yang +1
In noisy and reverberant environments, the performance of deep learning-based speech separation methods drops dramatically because previous methods are not designed and optimized f…
Review of end-to-end speech synthesis technology based on deep learning
Zhaoxi Mu, Xinyu Yang, Yizhuo Dong
As an indispensable part of modern human-computer interaction system, speech synthesis technology helps users get the output of intelligent machine more easily and intuitively, thu…