5 papers
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…
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…
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…