5 papers
WQ-Fusion: Dynamic Gated Attention for Cross-Domain Audio Representation
Mingda Lin, Lei Ding, Xinyue Zhou +6
While pre-trained models excel in specialized tasks, learning universal representations across diverse acoustic domains remains challenging. To address this, we propose WQ-Fusion,…
Joint Learning of Covariance Estimation and White Noise Gain for Robust MVDR Beamforming
Yongyi Deng, Hanchen Pei, Jianbo Ma +3
The minimum variance distortionless response (MVDR) beamformer is widely used for multichannel speech enhancement due to strong noise suppression while preserving target signals. I…
A Unified Neural Codec Language Model for Selective Editable Text to Speech Generation
Hanchen Pei, Shujie Liu, Yanqing Liu +5
Neural codec language models achieve impressive zero-shot Text-to-Speech (TTS) by fully imitating the acoustic characteristics of a short speech prompt, including timbre, prosody,…
Spatial-Filter-Bank-Based Neural Method for Multichannel Speech Enhancement
Tianqin Zheng, Jilu Jin, Hanchen Pei +3
The performance of deep learning-based multi-channel speech enhancement methods often deteriorates when the geometric parameters of the microphone array change. Traditional approac…
LMFCA-Net: A Lightweight Model for Multi-Channel Speech Enhancement with Efficient Narrow-Band and Cross-Band Attention
Yaokai Zhang, Hanchen Pei, Wanqi Wang +1
Deep learning based end-to-end multi-channel speech enhancement methods have achieved impressive performance by leveraging sub-band, cross-band, and spatial information. However, t…