Signal-dependent time-frequency analysis using a radially Gaussian kernel

A signal-dependent time-frequency representation: optimal kernel design

Time-frequency distributions (TFD’s), which indicate the energy content of a signal as a function of both time and frequency, are powerful tools for time-varying signal analysis. The lack of a single distribution that is “best” for all applications has resulted in a proliferation of TFD’s, each corresponding to a different, fixed mapping from signals to the time-frequency plane. A major drawbac...

Time-Frequency Signal Analysis Using Teager Energy

A Time-Frequency approach for EEG signal segmentation

The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non-stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert-Huang transform (HHT), is a new and powerful ...

A signal-dependent time-frequency representation: fast algorithm for optimal kernel design

A time-frequency representation based on an optimal, signal-dependent kernel has been proposed recently in an attempt to overcome one of the primary limitations of bilinear time-frequency distributions: that the best kernel and distribution depend on the signal to be analyzed. The optimization formulation for the signal-dependent kernel results in a linear program with a unique feature: a tree ...

A Signal-Dependent Evolution Kernel for Cohen Class Time-Frequency Distributions

Cohen class time–frequency distributions serve as alternatives to the traditional spectrogram and are known for their ability to provide simultaneous resolution in time and frequency. They employ a kernel along with the signal’s Wigner distribution. Kernel design has witnessed significant attention. Very recently Costa and BoudreauxBartels have proposed a multiform tiltable exponential distribu...