A comparison of smoothing kernels for computing bilinear time-frequency representations

Bilinear representations of Cohen´s class are expressed as the Wigner distribution (WD) of the signal convolved with a kernel defined as a 2-D window in time and frequency. Gaussian, exponential and cone kernels are compared by examining their local behavior in the time-frequency plane. It is demonstrated that kernel spread and localization properties are important concerns for analyzing time-varying signals. The cone kernel has good cross-term suppression for speech-like signals because of its large time extent. An effect of this is to enhance stationary terms and attenuate time-varying terms