1 citations · 2 across the 6 of their papers we have counts for
6 papers
SICRN: Advancing Speech Enhancement through State Space Model and Inplace Convolution Techniques
Changjiang Zhao, Shulin He, Xueliang Zhang
Speech enhancement aims to improve speech quality and intelligibility, especially in noisy environments where background noise degrades speech signals. Currently, deep learning met…
3S-TSE: Efficient Three-Stage Target Speaker Extraction for Real-Time and Low-Resource Applications
Shulin He, Jinjiang liu, Hao Li +3
Target speaker extraction (TSE) aims to isolate a specific voice from multiple mixed speakers relying on a registerd sample. Since voiceprint features usually vary greatly, current…
Efficient Multi-Channel Speech Enhancement with Spherical Harmonics Injection for Directional Encoding
Jiahui Pan, Pengjie Shen, Hui Zhang +1
Multi-channel speech enhancement extracts speech using multiple microphones that capture spatial cues. Effectively utilizing directional information is key for multi-channel enhanc…
Hierarchical Modeling of Spatial Cues via Spherical Harmonics for Multi-Channel Speech Enhancement
Jiahui Pan, Shulin He, Hui Zhang +1
Multi-channel speech enhancement utilizes spatial information from multiple microphones to extract the target speech. However, most existing methods do not explicitly model spatial…
PDPCRN: Parallel Dual-Path CRN with Bi-directional Inter-Branch Interactions for Multi-Channel Speech Enhancement
Jiahui Pan, Shulin He, Tianci Wu +2
Multi-channel speech enhancement seeks to utilize spatial information to distinguish target speech from interfering signals. While deep learning approaches like the dual-path convo…
LCSM: A Lightweight Complex Spectral Mapping Framework for Stereophonic Acoustic Echo Cancellation
Chenggang Zhang, Jinjiang Liu, Xueliang Zhang
The traditional adaptive algorithms will face the non-uniqueness problem when dealing with stereophonic acoustic echo cancellation (SAEC). In this paper, we first propose an effici…