6 papers
TQCodec: Towards neural audio codec for high-fidelity music streaming
Lixing He, Zhouxuan Chen, Mingshuai Liu +6
We propose TQCodec, a neural audio codec designed for high-bitrate, high-fidelity music streaming. Unlike existing neural codecs that primarily target ultra-low bitrates (<= 16kbps…
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…
FlowSE: Efficient and High-Quality Speech Enhancement via Flow Matching
Ziqian Wang, Zikai Liu, Xinfa Zhu +6
Generative models have excelled in audio tasks using approaches such as language models, diffusion, and flow matching. However, existing generative approaches for speech enhancemen…
LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
Boyi Kang, Xinfa Zhu, Zihan Zhang +10
Recent advancements in language models (LMs) have demonstrated strong capabilities in semantic understanding and contextual modeling, which have flourished in generative speech enh…
Distil-DCCRN: A Small-footprint DCCRN Leveraging Feature-based Knowledge Distillation in Speech Enhancement
Runduo Han, Weiming Xu, Zihan Zhang +2
The deep complex convolution recurrent network (DCCRN) achieves excellent speech enhancement performance by utilizing the audio spectrum's complex features. However, it has a large…
RaD-Net 2: A causal two-stage repairing and denoising speech enhancement network with knowledge distillation and complex axial self-attention
Mingshuai Liu, Zhuangqi Chen, Xiaopeng Yan +5
In real-time speech communication systems, speech signals are often degraded by multiple distortions. Recently, a two-stage Repair-and-Denoising network (RaD-Net) was proposed with…