The empirical mode decomposition (EMD) decomposes non-stationary signals that may
stem from nonlinear systems, in a local and fully data-driven manner. Noise-assisted …

This work reviews the advancements in the non-conventional analysis of speech signals,
particularly from an AM-FM analysis point of view. The benefits of such an analysis, as …

In this paper, a variational mode decomposition (VMD) based method has been proposed
for the instantaneous detection of voiced/non-voiced (V/NV) regions in the speech signals. In …

The current understanding of hydroclimatic processes is largely based on time series
analysis of observations such as river discharge. Although records of these variables are …

The decomposition of multi-component signals finds use in signal analysis. A new iterative
approach to decompose a multi-component non-stationary signal into amplitude–frequency …

The data-driven empirical mode decomposition (EMD) method is designed to analyze the
non-stationary signals like biomedical signals originating from nonlinear biological systems …

In this paper, a novel approach is proposed to determine instantaneous fundamental
frequency (IFF) of speech signals. In the proposed method, the detected voiced speech …

We propose a robust event-based method for estimation of the instantaneous fundamental
frequency of a voiced speech signal. The amplitude and frequency modulated (AM-FM) …

Detection of desaturations on the pulse oximetry signal is of great importance for the
diagnosis of sleep apneas. Using the counting of desaturations, an index can be built to help …

Empirical mode decomposition (EMD) has emerged as a powerful tool for signal/image
processing. However, extending the EMD to its three-dimensional (3D) version remains a …