Analysis & comparison of neural network training algorithms for the joint time-frequency analysis
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Techniques to obtain good resolution and concentrated time-frequency distributions: a review
EURASIP Journal on Advances in Signal Processing
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We present a method for estimating the generalized transfer function (GTF) of a time-varying filter from a time-frequency representation (TFR) of its output. This method uses the fact that many TFR's can be written as blurred versions of the GTF. The approach minimizes 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 signals and to signal masking