GAUSSIAN MODELING OF MIXTURES OF NON-STATIONARY SIGNALS IN THE TIME-FREQUENCY DOMAIN (HR-NMF)

Nonnegative Matrix Factorization (NMF) is a powerful tool for decomposing mixtures of non-stationary signals in the Time-Frequency (TF) domain. However, unlike the High Resolution (HR) methods dedicated to mixtures of exponentials, its spectral resolution is limited by that of the underlying TF representation. In this paper, we propose a unified probabilistic model called HR-NMF, that permits to overcome this limit by taking both phases and local correlations in each frequency band into account. This model is estimated with a recursive implementation of the EM algorithm, that is successfully applied to source separation and audio inpainting.