In reverberant conditions with multiple concurrent speakers, each microphone acquires a
mixture signal of multiple speakers at a different location. In over-determined conditions …

Time-frequency (TF) domain dual-path models achieve high-fidelity speech separation.
While some previous state-of-the-art (SoTA) models rely on RNNs, this reliance means they …

The last decade has witnessed significant advancements in deep learning-based speech
enhancement (SE). However, most existing SE research has limitations on the coverage of …

Building a single universal speech enhancement (SE) system that can handle arbitrary input
is a demanded but underexplored research topic. Towards this ultimate goal, one direction …

Recent advancements in language models have significantly enhanced performance in
multiple speech-related tasks. Existing speech language models typically utilize task …

Recent speech enhancement models have shown impressive performance gains by scaling
up model complexity and training data. However, the impact of dataset variability (eg text …

Diffusion-based generative models have recently achieved remarkable results in speech
and vocal enhancement due to their ability to model complex speech data distributions …

Several attempts have been made to handle multiple source separation tasks such as
speech enhancement, speech separation, sound event separation, music source separation …

Noise robustness is critical when applying automatic speech recognition (ASR) in real-world
scenarios. One solution involves the used of speech enhancement (SE) models as the front …

Deep learning-based speech enhancement (SE) models have achieved impressive
performance in the past decade. Numerous advanced architectures have been designed to …