  This paper addresses the problem of estimating the modes of an observed
non-stationary mixture signal in the presence of an arbitrary distributed
noise. A novel Bayesian model is introduced to estimate the model parameters
from the spectrogram of the observed signal, by resorting to the stochastic
version of the EM algorithm to avoid the computationally expensive joint
parameters estimation from the posterior distribution. The proposed method is
assessed through comparative experiments with state-of-the-art methods. The
obtained results validate the proposed approach by highlighting an improvement
of the modes estimation performance.
