1 citations · 1 across the 9 of their papers we have counts for
9 papers
REVE: Efficient Hallucination Correction for Large Audio-Language Models via Reused Encoder States
Hongjin Song, Jiasheng Kuang, Xinyu Yang +4
Large audio-language models may mention acoustic events that are absent from the input. A separate audio event detector can verify these mentions, but doing so requires a second au…
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…
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…