1 citations · 1 across the 9 of their papers we have counts for
9 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…
RaD-Net: A Repairing and Denoising Network for Speech Signal Improvement
Mingshuai Liu, Zhuangqi Chen, Xiaopeng Yan +5
This paper introduces our repairing and denoising network (RaD-Net) for the ICASSP 2024 Speech Signal Improvement (SSI) Challenge. We extend our previous framework based on a two-s…
Two-stage Neural Network for ICASSP 2023 Speech Signal Improvement Challenge
Mingshuai Liu, Shubo Lv, Zihan Zhang +6
In ICASSP 2023 speech signal improvement challenge, we developed a dual-stage neural model which improves speech signal quality induced by different distortions in a stage-wise div…
Two-step Band-split Neural Network Approach for Full-band Residual Echo Suppression
Zihan Zhang, Shimin Zhang, Mingshuai Liu +4
This paper describes a Two-step Band-split Neural Network (TBNN) approach for full-band acoustic echo cancellation. Specifically, after linear filtering, we split the full-band sig…