Maximum likelihood noise estimation for spectrogram segmentation control

This communication is composed of two related parts. First we propose an approximation to the Maximum Likelihood estimator of the γ distribution parameters. We show that it leads to build an efficient estimator of a white Gaussian process variance by noting that γ distribution admits sufficient statistics. Second we describe an application of this result to a non-stationary signal spectrogram segmentation that we proposed recently. Examples of segmented spectrograms are presented on a synthetic signal and on an acoustical recording of a dolphin whistle.