When designing fully-convolutional neural network, there is a trade-off between receptive
field size, number of parameters and spatial resolution of features in deeper layers of the …

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 …

Speech enhancement performance is highly reliant on on the efficacy of representative
features extracted from noisy speech. However, SE frequently experiences problems with …

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 …

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 …

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 …

In this article a new neural network for speech enhancement is proposed where single-
channel noisy speech is processed in order to improve its intelligibility and quality. It is …

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

Deep learning has recently made a breakthrough in the speech enhancement process.
Some architectures are based on a time domain representation, while others operate in the …

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