Redundant time-frequency marginals for chirplet decomposition

Abstract

This paper presents the foundations of a novel method for chirplet signal decomposition. In contrast to basis-pursuit techniques on over-complete dictionaries, the proposed method uses a reduced set of adaptive parametric chirplets. The estimation criterion corresponds to the maximization of the likelihood of the chirplet parameters from redundant time-frequency marginals. The optimization algorithm that results from this scenario combines Gaussian mixture models and Huber's robust regression in an iterative fashion. Simulation results support the proposed avenue.