The query-based audio separation usually employs specific queries to extract target sources
from a mixture of audio signals. Currently, most query-based separation models need …

This paper introduces a new Dynamic Gated Recurrent Neural Network (DG-RNN) for
compute-efficient speech enhancement models running on resource-constrained hardware …

Computational complexity is critical when deploying deep learning-based speech denoising
models for on-device applications. Most prior research focused on optimizing model …

The proliferation of deep neural networks has spawned the rapid development of acoustic
echo cancellation and noise suppression, and plenty of prior arts have been proposed …

With the rapid development of deep learning, a large number of models with excellent
performance for speech separation tasks have emerged in the literature. Despite their …

Echo delay estimation (EDE) serves as a preprocessing component within an acoustic echo
canceller (AEC). Despite some progress over the past few decades, there is a dearth of …

Audio generation is a major branch of generative AI research. Compared with prior works in
this area that are commonly task-specific with heavy domain knowledge, this paper …

Deep learning residue echo suppression (RES) exhibits superior performance compared
with traditional methods in recent years. However, a low-resource system or preemptive …