The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

Frequency-domain monaural speech enhancement has been extensively studied for over
60 years, and a great number of methods have been proposed and applied to many …

Removing background noise from speech audio has been the subject of considerable effort,
especially in recent years due to the rise of virtual communication and amateur recordings …

Deep complex convolution recurrent network (DCCRN), which extends CRN with complex
structure, has achieved superior performance in MOS evaluation in Interspeech 2020 deep …

Speech quality is often degraded by acoustic echoes, background noise, and reverberation.
In this paper, we propose a system consisting of deep learning and signal processing to …

In the field of speech enhancement, time domain methods have difficulties in achieving both
high performance and efficiency. Recently, dual-path models have been adopted to …

In this work, we further develop the conformer-based metric generative adversarial network
(CMGAN) model 1 for speech enhancement (SE) in the time-frequency (TF) domain. This …

Speech enhancement plays an essential role in a wide range of speech processing
applications. Recent studies on speech enhancement tend to investigate how to effectively …

With the development of deep neural networks, speech enhancement technology has been
vastly improved. However, commonly used speech enhancement approaches cannot fully …

Speech enhancement (SE) aims to suppress the additive noise from noisy speech signals to
improve the speech's perceptual quality and intelligibility. However, the over-suppression …