1 citations · 2 across the 9 of their papers we have counts for
9 papers
Masked Audio Modeling with CLAP and Multi-Objective Learning
Yifei Xin, Xiulian Peng, Yan Lu
Most existing masked audio modeling (MAM) methods learn audio representations by masking and reconstructing local spectrogram patches. However, the reconstruction loss mainly accou…
DasFormer: Deep Alternating Spectrogram Transformer for Multi/Single-Channel Speech Separation
Shuo Wang, Xiangyu Kong, Xiulian Peng +3
For the task of speech separation, previous study usually treats multi-channel and single-channel scenarios as two research tracks with specialized solutions developed respectively…
Contrast-PLC: Contrastive Learning for Packet Loss Concealment
Huaying Xue, Xiulian Peng, Yan Lu
Packet loss concealment (PLC) is challenging in concealing missing contents both plausibly and naturally when there are only limited available context to use. Recently deep-learnin…
Time-Variance Aware Real-Time Speech Enhancement
Chengyu Zheng, Yuan Zhou, Xiulian Peng +2
Time-variant factors often occur in real-world full-duplex communication applications. Some of them are caused by the complex environment such as non-stationary environmental noise…
Improving Speech Enhancement via Event-based Query
Yifei Xin, Xiulian Peng, Yan Lu
Existing deep learning based speech enhancement (SE) methods either use blind end-to-end training or explicitly incorporate speaker embedding or phonetic information into the SE ne…
Real-time speech enhancement with dynamic attention span
Chengyu Zheng, Yuan Zhou, Xiulian Peng +2
For real-time speech enhancement (SE) including noise suppression, dereverberation and acoustic echo cancellation, the time-variance of the audio signals becomes a severe challenge…