8 citations · 9 across the 16 of their papers we have counts for
17 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…
The ISCSLP 2026 Real-World Audio-Visual Speech Enhancement Challenge: A Benchmark with Natural Mixtures and Degraded Video
Kai Li, Wenze Ren, Junjie Li +11
Audio-visual speech enhancement (AVSE) uses a target speaker's visible articulation to recover that speaker's voice from overlapped speech. Yet most evaluation protocols rely on sy…
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 Knowledge-Driven Approach to Target Speech Extraction in the Presence of Background Sound Effects for Cinematic Audio Source Separation (CASS)
Chun-wei Ho, Sabato Marco Siniscalchi, Kai Li +1
We propose a knowledge-driven approach to speech target extraction in the presence of background sound effects already recorded in cinematic audio. The specific knowledge sources s…
Dual-View Predictive Diffusion: Lightweight Speech Enhancement via Spectrogram-Image Synergy
Ke Xue, Rongfei Fan, Kai Li +3
Diffusion models have recently set new benchmarks in Speech Enhancement (SE). However, most existing score-based models treat speech spectrograms merely as generic 2D images, apply…
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-…
BridgeVoC: Revitalizing Neural Vocoder from a Restoration Perspective
Andong Li, Tong Lei, Rilin Chen +5
This paper revisits the neural vocoder task through the lens of audio restoration and propose a novel diffusion vocoder called BridgeVoC. Specifically, by rank analysis, we compare…
Unsupervised Single-Channel Speech Separation with Diffusion under Speaker-Embedding Guidance
Runwu Shi, Kai Li, Yiyan Wang +5
Speech separation is a fundamental task in audio processing, typically addressed with fully supervised systems trained on paired mixtures. While effective, such systems typically r…
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…
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…
SepPrune: Structured Pruning for Efficient Deep Speech Separation
Yuqi Li, Kai Li, Xin Yin +6
Although deep learning has substantially advanced speech separation in recent years, most existing studies continue to prioritize separation quality while overlooking computational…
SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline
Helin Wang, Jiarui Hai, Dongchao Yang +7
Target Speech Extraction (TSE) aims to isolate a target speaker's voice from a mixture of multiple speakers by leveraging speaker-specific cues, typically provided as auxiliary aud…
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…