High-resolution time-frequency representation with generative adversarial networks

Zeynel Deprem, A. Enis Çetin

Abstract: Signal representation in time-frequency (TF) domain is valuable in many applications including radar imaging and inverse synthetic aperture radar. TF representation allows us to identify signal components or features in a mixed time and frequency plane. There are several well-known tools, such as Wigner–Ville Distribution (WVD), short-time Fourier transform and various other variants for such a purpose. The main requirement for a TF representation tool is to give a high-resolution view of the signal such that the signal components or features are identifiable. A commonly used method is the reassignment process which reduces the cross-terms by artificially moving smoothed WVD values from their actual location to the center of the gravity for that region. In this article, we propose a novel reassignment method using the conditional generative adversarial network (CGAN). We train a CGAN to perform the reassignment process. Through examples, it is shown that the method generates high-resolution TF representations which are better than the current reassignment methods.

External IDs:dblp:journals/sivp/DepremC23