2 citations · 2 across the 11 of their papers we have counts for
12 papers
NeurDuo-EEG: A Long-Sequence EEG Foundation Model with Persistent State and Explicit Memory
Yifan Wang, Haiping Liu, Yang Cui +13
Electroencephalography (EEG) is recorded continuously over hours, with relevant dynamics spanning timescales from milliseconds to hours. Most EEG foundation models nevertheless pro…
DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement
Cunhang Fan, Enrui Liu, Jing Zhou +6
Although artificial neural network (ANN) based speech enhancement (SE) methods demonstrate excellent performance, the high computational complexity and high energy consumption hind…
RTCFake: Speech Deepfake Detection in Real-Time Communication
Jun Xue, Zhuolin Yi, Yihuan Huang +6
With the rapid advancement of speech generation technologies, the threat posed by speech deepfakes in real-time communication (RTC) scenarios has intensified. However, existing det…
DMF2Mel: A Dynamic Multiscale Fusion Network for EEG-Driven Mel Spectrogram Reconstruction
Cunhang Fan, Sheng Zhang, Jingjing Zhang +4
Decoding speech from brain signals is a challenging research problem. Although existing technologies have made progress in reconstructing the mel spectrograms of auditory stimuli a…
M3ANet: Multi-scale and Multi-Modal Alignment Network for Brain-Assisted Target Speaker Extraction
Cunhang Fan, Ying Chen, Jian Zhou +6
The brain-assisted target speaker extraction (TSE) aims to extract the attended speech from mixed speech by utilizing the brain neural activities, for example Electroencephalograph…
RPRA-ADD: Forgery Trace Enhancement-Driven Audio Deepfake Detection
Ruibo Fu, Xiaopeng Wang, Zhengqi Wen +8
Existing methods for deepfake audio detection have demonstrated some effectiveness. However, they still face challenges in generalizing to new forgery techniques and evolving attac…
MHANet: Multi-scale Hybrid Attention Network for Auditory Attention Detection
Lu Li, Cunhang Fan, Hongyu Zhang +4
Auditory attention detection (AAD) aims to detect the target speaker in a multi-talker environment from brain signals, such as electroencephalography (EEG), which has made great pr…
ListenNet: A Lightweight Spatio-Temporal Enhancement Nested Network for Auditory Attention Detection
Cunhang Fan, Xiaoke Yang, Hongyu Zhang +4
Auditory attention detection (AAD) aims to identify the direction of the attended speaker in multi-speaker environments from brain signals, such as Electroencephalography (EEG) sig…
SSM2Mel: State Space Model to Reconstruct Mel Spectrogram from the EEG
Cunhang Fan, Sheng Zhang, Jingjing Zhang +2
Decoding speech from brain signals is a challenging research problem that holds significant importance for studying speech processing in the brain. Although breakthroughs have been…
Improved Feature Extraction Network for Neuro-Oriented Target Speaker Extraction
Cunhang Fan, Youdian Gao, Zexu Pan +4
The recent rapid development of auditory attention decoding (AAD) offers the possibility of using electroencephalography (EEG) as auxiliary information for target speaker extractio…
BSDB-Net: Band-Split Dual-Branch Network with Selective State Spaces Mechanism for Monaural Speech Enhancement
Cunhang Fan, Enrui Liu, Andong Li +5
Although the complex spectrum-based speech enhancement(SE) methods have achieved significant performance, coupling amplitude and phase can lead to a compensation effect, where ampl…
LiSenNet: Lightweight Sub-band and Dual-Path Modeling for Real-Time Speech Enhancement
Haoyin Yan, Jie Zhang, Cunhang Fan +2
Speech enhancement (SE) aims to extract the clean waveform from noise-contaminated measurements to improve the speech quality and intelligibility. Although learning-based methods c…