# High Resolution Time-Frequency Generation with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2106.00668 · HTML: https://mlsanity.com/paper/2106.00668/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2021.

## Built on by (most cited first, 1 of 1)

- [Low-Rank Adaptation of Pre-Trained Stable Diffusion for Rigid-Body Target ISAR Imaging](https://mlsanity.com/paper/2503.21823/) (2025)

## Abstract (arXiv)

Signal representation in Time-Frequency (TF) domain is valuable in many applications including radar imaging and inverse synthetic aparture 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 (STFT) 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.