Parameter estimation of Gaussian functions using the scaled reassigned spectrogram

In this paper we suggest an improved algorithm for estimation of parameters detailing Gaussian functions and expand it to handle linear combinations of Gaussian functions. Components in the signal are first detected in the spectrogram, which is calculated using a Gaussian window function. Scaled reassignment is then performed using a set of candidate scaling factors and the local Renyi entropy is used to measure the concentration of each component using every candidate scaling factor. Exploiting the fact that a Gaussian function may be perfectly reassigned into one single point given the correct scaling, one may identify the parameters detailing the Gaussian function. We evaluate the algorithm on both simulated and real data.