Deep neural networks (DNN) have remarkably progressed in applications involving large
and complex datasets but have been criticized as a black-box. This downside has recently …

The classification of surface electromyography (sEMG) signal has an important usage in the
man-machine interfaces for proper controlling of prosthetic devices with multiple degrees of …

Single channel speech enhancement has reached a great progress recently with the
development of deep learning. However, it is still a challenging problem to achieve …

Human speech in real-world environments is typically degraded by the background noise.
They have a negative impact on perceptual speech quality and intelligibility which causes …

A speech enhancement algorithm improves the perceptual aspects of a speech degraded by
noise signals. We propose a phase-aware deep neural network (DNN) using the regularized …

Fuzzy inference systems (FISs) are a predictive modeling technique based on fuzzy sets that
utilize approximate reasoning to mimic the decision-making process of human experts …

Speech signals reaching our ears are in general contaminated by the background noise
distortion which is detrimental to both speech quality and intelligibility. In this paper, we …

Speech-in-noise perception is an important research problem in many real-world multimedia
applications. The noise-reduction methods contributed significantly; however rely on a priori …

Speech signal enhancement is a subject of study in which a large number of researchers
are working to improve the quality and perceptibility of speech signals. In the existing …

Map** and Masking targets are both widely used in recent Deep Neural Network (DNN)
based supervised speech enhancement. Masking targets are proved to have a positive …