  Nonnegative Matrix Factorization (NMF) is a powerful tool for decomposing
mixtures of audio signals in the Time-Frequency (TF) domain. In applications
such as source separation, the phase recovery for each extracted component is a
major issue since it often leads to audible artifacts. In this paper, we
present a methodology for evaluating various NMF-based source separation
techniques involving phase reconstruction. For each model considered, a
comparison between two approaches (blind separation without prior information
and oracle separation with supervised model learning) is performed, in order to
inquire about the room for improvement for the estimation methods. Experimental
results show that the High Resolution NMF (HRNMF) model is particularly
promising, because it is able to take phases and correlations over time into
account with a great expressive power.
