  This paper presents a novel approach for estimating the modes of an observed
non-stationary mixture signal. A link is first established between the
short-time Fourier transform and the sparse sampling theory, where the
observations are modeled as a stream of pulses filtered by a known function. As
the signal to retrieve has a finite rate of innovation (FRI), an adapted
reconstruction approach is used to estimate the signal modes in the presence of
noise. We compare our results with state-of-the-art methods and validate our
approach by highlighting an improvement of the estimation performance in
different scenarios. Our approach paves the way of future FRI-based mode
disentangling algorithms.
