Generalized transfer function estimation using evolutionary spectral deblurring

We present a new technique for estimating the generalized transfer function (GTF) of a time-varying filter from time-frequency representations (TFRs) of its output. We use the fact that many of these representations can be written as blurred versions of the GTF. The approach consists in minimizing the error between the TFR found from the data and that found by blurring the GTF. The problem as such has many solutions. We, therefore, additionally constrain it to minimize the distance between the GTF-based spectrum and the autoterms of the Wigner distribution, suppressing the cross terms using an appropriate signal dependent mask function. To illustrate the performance of the proposed procedure we apply it to the spectral representation of speech and to signal masking and demonstrate its superior performance over the existing methods.