The aim of speech enhancement is to improve speech signal quality and intelligibility from a
noisy microphone signal. In many applications, it is crucial to enable processing with small …

In the recent past, deep neural network (DNN) approaches have achieved significant
improvements in speech enhancement. However, the improved performance is …

Deep learning based single-channel speech enhancement tries to train a neural network
model for the prediction of clean speech signal. There are a variety of popular network …

The Dual-Path Convolution Recurrent Network (DPCRN) was proposed to effectively exploit
time-frequency domain information. By combining the DPRNN module with Convolution …

Masking-based and spectrum mapping-based methods are the two main algorithms of
speech enhancement with deep neural network (DNN). But the mapping-based methods …

Single-channel speech enhancement aims to remove the interfering noise and
reverberation in real environments by a single microphone, which is a very challenging task …

Deep learning (DL) networks have grown into powerful alternatives for speech
enhancement and have achieved excellent results by improving speech quality …

Currently, most of the speech enhancement methods can't address the performance
degradation problem caused by low signal-to-noise ratios (SNR) and non-stationary noises …

Single-channel speech enhancement with deep neural networks (DNNs) has shown
promising performance and is thus intensively being studied. In this paper, instead of …

While modern deep learning-based models have significantly outperformed traditional
methods in the area of speech enhancement, they often necessitate a lot of parameters and …