At present, the vast majority of building blocks, techniques, and architectures for deep
learning are based on real-valued operations and representations. However, recent work on …

Deep learning (DL) has been recognized as an effective solution for automatic modulation
classification (AMC). However, most recent DL based AMC works are based on real-valued …

In contrast to the conventional minimum mean square error (MMSE)-based noise reduction
techniques, we propose a supervised method to enhance speech by means of finding a …

Artificial neural networks (ANNs) based machine learning models and especially deep
learning models have been widely applied in computer vision, signal processing, wireless …

Typical speech enhancement methods, based on the short-time Fourier analysis-
modification-synthesis (AMS) framework, modify only the magnitude spectrum and keep the …

A single dendritic neuron model (DNM) that owns the nonlinear information processing
ability of dendrites has been widely used for classification and prediction. Complex-valued …

In synthetic aperture radar automatic target recognition (SAR-ATR), target information is
usually propagated and reserved in complex-valued form, namely, magnitude information …

In conventional speaker recognition methods based on Mel-frequency cepstral coefficients
(MFCCs), phase information has hitherto been ignored. In this paper, we propose a phase …

In this paper, by using the quaternion algebra, the conventional complex-type moments
(CTMs) for gray-scale images are generalized to color images as quaternion-type moments …

During the past three decades, the issue of processing spectral phase has been largely
neglected in speech applications. There is no doubt that the interest of speech processing …