We develop an interpretable and learnable Wigner-Ville distribution that produces a super-resolved quadratic signal representation for time-series analysis. Our approach has two main hallmarks. First, it interpolates between known time-frequency representations (TFRs) in that it can reach super-resolution with increased time and frequency resolution beyond what the Heisenberg uncertainty principle prescribes and thus beyond commonly employed TFRs, Second, it is interpretable thanks to an explicit low-dimensional and physical parameterization of the Wigner-Ville distribution. We demonstrate that our approach is able to learn highly adapted TFRs and is ready and able to tackle various large-scale classification tasks, where we reach state-of-the-art performance compared to baseline and learned TFRs.
@article{arxiv.2006.07713,
title = {Interpretable Super-Resolution via a Learned Time-Series Representation},
author = {Randall Balestriero and Herve Glotin and Richard G. Baraniuk},
journal= {arXiv preprint arXiv:2006.07713},
year = {2020}
}