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 …

Recent advancements in deep learning-based speech enhancement models have
extensively used attention mechanisms to achieve state-of-the-art methods by …

One of the leading speech enhancement technologies is the deep neural network-based
approach, which dominates the recent development in single-channel speech …

In recent decades, neural network based methods have significantly improved the
performance of speech enhancement. Most of them estimate time-frequency (TF) …

One of the strengths of traditional convolutional neural networks (CNNs) is their inherent
translational invariance. However, for the task of speech enhancement in the timefrequency …

Single-channel speech enhancement (SE) can be described, in its simplest terms, as
learning a transformation from single-channel noisy speech to the clean speech. To do this …

The data driven speech enhancement approaches using regression-based deep neural
network usually result in enormous number of model parameters, which increase the …

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

In this paper, we present a deep neural network architecture comprising of both
convolutional neural network (CNN) and recurrent neural network (RNN) layers for real-time …

Over the past 10 years, deep learning has enabled significant advancements in the
improvement of noisy speech. In an end-to-end speech enhancement, the deep neural …