lchodgson@watale.uwaterloo.ca (Lauren Hodgson) (07/27/90)
Hello.... I'm applying various bilinear transforms/distributions (such as Wigner, Choi-Williams, etc) to speech and am pondering some of the rarely mentioned practical details. Specifically, bilinear transforms take a window of time domain data, multiply the data points' amplitudes together (with various combinations and scalings of the amplitudes of the data points depending on the particular bilinear kernel used), and then typically take the FFT of the newly constructed data to provide the Time-Freq distribution. How should the original time domain signal points be scaled and offset wrt 0.0 amplitude such that the bilinear tranform's initial multiplication of data points in the time domain has the optimum effect in extracting useful spectral information in the final T-F output distribution? 1) Intuitively and empirically, setting the offset so that all time domain data is positive before doing the multiply seems to badly distort the output spectrum. 2) Setting the offset to remove the root of the DC over the window does not work as well as 3 below. 3) Setting the offset to remove DC over the window seems to work fairly well..... but is there something better? Ideas? Thanks. Lauren Hodgson Dept of Systems Design Engineering University of Waterloo