10 papers
A Brain-Inspired Deep Separation Network for Single Channel Raman Spectra Unmixing
Gaoruishu Long, Jinchao Liu, Bo Liu +2
Raman spectra obtained in real world applications are often a noisy combination of several spectra of various substances in a tested sample. Unmixing such spectra into individual c…
A-LLM: An End-to-end Conversational Audio Avatar Large Language Model
Xiaolin Hu, Hang Yuan, Xinzhu Sang +4
Developing expressive and responsive conversational digital humans is a cornerstone of next-generation human-computer interaction. While large language models (LLMs) have significa…
FGNet: Leveraging Feature-Guided Attention to Refine SAM2 for 3D EM Neuron Segmentation
Zhenghua Li, Hang Chen, Zihao Sun +2
Accurate segmentation of neural structures in Electron Microscopy (EM) images is paramount for neuroscience. However, this task is challenged by intricate morphologies, low signal-…
Efficient Audio-Visual Speech Separation with Discrete Lip Semantics and Multi-Scale Global-Local Attention
Kai Li, Kejun Gao, Xiaolin Hu
Audio-visual speech separation (AVSS) methods leverage visual cues to extract target speech and have demonstrated strong separation quality in noisy acoustic environments. However,…
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…
Enhancing Spectrogram Realism in Singing Voice Synthesis via Explicit Bandwidth Extension Prior to Vocoder
Runxuan Yang, Kai Li, Guo Chen +1
This paper addresses the challenge of enhancing the realism of vocoder-generated singing voice audio by mitigating the distinguishable disparities between synthetic and real-life r…