12 citations · 24 across the 6 of their papers we have counts for
4 papers · 1 filter
DuSpaR: Dual-State Sparsifying Recurrent Unit with Feedback Modulation for Compute-Efficient Speech Processing
Zixiao Li, Sheng Zhou, Longbiao Cheng +1
We introduce the Dual-state Sparsifying Recurrent Unit (DuSpaR) as a computationally efficient building block for speech processing models on resource-constrained edge devices. It…
Towards Lightweight Adaptation of Speech Enhancement Models in Real-World Environments
Longbiao Cheng, Shih-Chii Liu
Recent studies have shown that post-deployment adaptation can improve the robustness of speech enhancement models in unseen noise conditions. However, existing methods often incur…
Modulating State Space Model with SlowFast Framework for Compute-Efficient Ultra Low-Latency Speech Enhancement
Longbiao Cheng, Ashutosh Pandey, Buye Xu +3
Deep learning-based speech enhancement (SE) methods often face significant computational challenges when needing to meet low-latency requirements because of the increased number of…
Dynamic Gated Recurrent Neural Network for Compute-efficient Speech Enhancement
Longbiao Cheng, Ashutosh Pandey, Buye Xu +2
This paper introduces a new Dynamic Gated Recurrent Neural Network (DG-RNN) for compute-efficient speech enhancement models running on resource-constrained hardware platforms. It l…