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This paper presents a novel approach for estimating the modes of an observednon-stationary mixture signal. A link is first established between theshort-time Fourier transform and the sparse sampling theory, where theobservations are modeled as a stream of pulses filtered by a known function. Asthe signal to retrieve has a finite rate of innovation (FRI), an adaptedreconstruction approach is used to estimate the signal modes in the presence ofnoise. We compare our results with state-of-the-art methods and validate ourapproach by highlighting an improvement of the estimation performance indifferent scenarios. Our approach paves the way of future FRI-based modedisentangling algorithms.

Time-Frequency Ridge Estimation of Multi-Component Signals using Sparse Modeling of Signal Innovation