8 citations · 9 across the 14 of their papers we have counts for
15 papers
Learning Where to Split: Dynamic Band Splitting for Audio Source Separation
Bingzhe Zhang, Kai Li, Xiaolin Hu
Audio source separation models must preserve fine spectral structure while keeping the cost of the separator tractable. Band-split architectures reduce this cost by manually partit…
CORAL: A Benchmark for Structure-aware and Brain-wide Neuron Reconstruction in Light Microscopy
Zekang Yang, Jiamin Li, Zhenghua Li +3
Automatic neuron reconstruction from light microscopy images is a central problem in computational neuroanatomy. While recent methods have achieved encouraging results on local ima…
The SonicAGI System for the REAL-TSE Challenge
Kai Li, Wendi Sang, Jintao Cheng +1
Real-world target speaker extraction (TSE) remains challenging because target speech, interference, and enrollment are recorded under mismatched acoustic conditions with reverberat…
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…
A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation
Kai Li, Jintao Cheng, Chang Zeng +5
Query-based universal sound separation is fundamental to intelligent auditory systems, aiming to isolate specific sources from mixtures. Despite recent advances, existing methods c…
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…
A Fast and Lightweight Model for Causal Audio-Visual Speech Separation
Wendi Sang, Kai Li, Runxuan Yang +2
Audio-visual speech separation (AVSS) aims to extract a target speech signal from a mixed signal by leveraging both auditory and visual (lip movement) cues. However, most existing…
Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation
Guo Chen, Kai Li, Runxuan Yang +1
Existing causal speech separation models often underperform compared to non-causal models due to difficulties in retaining historical information. To address this, we propose the T…
SonicSim: A customizable simulation platform for speech processing in moving sound source scenarios
Kai Li, Wendi Sang, Chang Zeng +3
Systematic evaluation of speech separation and enhancement models under moving sound source conditions requires extensive and diverse data. However, real-world datasets often lack…
TIGER: Time-frequency Interleaved Gain Extraction and Reconstruction for Efficient Speech Separation
Mohan Xu, Kai Li, Guo Chen +1
In recent years, much speech separation research has focused primarily on improving model performance. However, for low-latency speech processing systems, high efficiency is equall…
SPMamba: State-space model is all you need in speech separation
Kai Li, Guo Chen, Runxuan Yang +1
Existing CNN-based speech separation models face local receptive field limitations and cannot effectively capture long time dependencies. Although LSTM and Transformer-based speech…