3 citations · 5 across the 6 of their papers we have counts for
6 papers
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…
Restorative Speech Enhancement: A Progressive Approach Using SE and Codec Modules
Hsin-Tien Chiang, Hao Zhang, Yong Xu +2
In challenging environments with significant noise and reverberation, traditional speech enhancement (SE) methods often lead to over-suppressed speech, creating artifacts during li…
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…
Neural Network Augmented Kalman Filter for Robust Acoustic Howling Suppression
Yixuan Zhang, Hao Zhang, Meng Yu +1
Acoustic howling suppression (AHS) is a critical challenge in audio communication systems. In this paper, we propose a novel approach that leverages the power of neural networks (N…
Advancing Acoustic Howling Suppression through Recursive Training of Neural Networks
Hao Zhang, Yixuan Zhang, Meng Yu +1
In this paper, we introduce a novel training framework designed to comprehensively address the acoustic howling issue by examining its fundamental formation process. This framework…
Unifying Robustness and Fidelity: A Comprehensive Study of Pretrained Generative Methods for Speech Enhancement in Adverse Conditions
Heming Wang, Meng Yu, Hao Zhang +5
Enhancing speech signal quality in adverse acoustic environments is a persistent challenge in speech processing. Existing deep learning based enhancement methods often struggle to…