Super-Resolution from Short-Time Fourier Transform Measurements

While spike trains are obviously not band-limited, the theory of super-resolution tells us that perfect recovery of unknown spike locations and weights from low-pass Fourier transform measurements is possible provided that the minimum spacing, $\mathrm{\Delta }$, between spikes is not too small. Specifically, for a cutoff frequency of $f_c$, Donoho [2] shows that exact recovery is possible if $\mathrm{\Delta }>1\mathrm{/}{f}_c$, but does not specify a corresponding recovery method. On the other hand, Cand`es and Fernandez-Granda [3] provide a recovery method based on convex optimization, which provably succeeds as long as $\Delta

2/f_c$. In practical applications one often has access to windowed Fourier transform measurements, i.e., short-time Fourier transform (STFT) measurements, only. In this paper, we develop a theory of super-resolution from STFT measurements, and we propose a method that provably succeeds in recovering spike trains from STFT measurements provided that $\mathrm{\Delta }>1\mathrm{/}{f}_c$.