  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.
