EM-driven stereo-like Gaussian chirplet mixture estimation

This paper proposes a novel technique for the time-frequency (TF) estimation of a Gaussian chirplet mixture in a signal. The technique relies on the combination of one and a half-worth of chirp transform of the signal and an expectation maximization-driven blind identification algorithm, in such a way that it carries out the chirplet estimation in-segment. The mechanism resembles stereo processing while keeping an interesting biological parallel with auditory cortex artificial models. This technique applied on a short-time processing basis is shown to be accurate at identifying the TF composition of a bat echolocation chirp. The proposed technique represents the initial basis for the development of a parallel hierarchic processing for more ambitious objectives.