6 papers · 1 filter
G-MaP-SE: Guided Speech Enhancement via GMM-Based Prior Matching
Yike Zhu, Ziqian Wang, Zikai Liu +5
Using speaker embeddings as conditioning can strengthen speech enhancement, but most methods either require clean enrollment audio or rely on embeddings extracted from noisy speech…
EvoTSE: Evolving Enrollment for Target Speaker Extraction
Zikai Liu, Ziqian Wang, Xingchen Li +4
Target Speaker Extraction (TSE) aims to isolate a specific speaker's voice from a mixture, guided by a pre-recorded enrollment. While TSE bypasses the global permutation ambiguity…
SenSE: Semantic-Aware High-Fidelity Universal Speech Enhancement
Xingchen Li, Hanke Xie, Ziqian Wang +4
Generative Universal Speech Enhancement (USE) methods aim to leverage generative models to improve speech quality under various types of distortions. However, existing generative s…
UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling
Ziqian Wang, Zikai Liu, Yike Zhu +6
Generative modeling has recently achieved remarkable success across image, video, and audio domains, demonstrating powerful capabilities for unified representation learning. Yet sp…
EchoFree: Towards Ultra Lightweight and Efficient Neural Acoustic Echo Cancellation
Xingchen Li, Boyi Kang, Ziqian Wang +4
In recent years, neural networks (NNs) have been widely applied in acoustic echo cancellation (AEC). However, existing approaches struggle to meet real-world low-latency and comput…
DualSep: A Light-weight dual-encoder convolutional recurrent network for real-time in-car speech separation
Ziqian Wang, Jiayao Sun, Zihan Zhang +3
Advancements in deep learning and voice-activated technologies have driven the development of human-vehicle interaction. Distributed microphone arrays are widely used in in-car sce…