Window length selection for smoothing the Wigner distribution by applying an adaptive filter technique

A method for the selection of window parameters in the WD (Wigner distribution) domain is presented. The amount of smoothing can be controlled by varying the window lengths in both the frequency and time domains independently. The authors propose to estimate the window length by estimating the center frequency and center time of the WD of each signal component, using the block least-mean-square (BLMS) algorithm coupled with an unsupervised clustering technique. The window parameters are updated adaptively in order to obtain adequate smoothing in the case of nonstationary signals. A smoothing factor is introduced to obtain a measure of smoothing. Examples are given of applying this method to multicomponent synthetic signals and actual speech data