“…For this purpose, supervised approaches based on e.g., nonnegative matrix factorization (NMF) [20], [21] or its probabilistic formulation known as Probabilistic Latent Component Analysis (PLCA) [13], [22], where retrieved examples can be used to pre-learn the spectral dictionaries of the corresponding sources, are of great interest. Other methods in the prior art that couple the decomposition of the reference signals together with the mixture could also be considered [8], [11], [12], [23]. Regardless of the approach, several challenges, as detailed in Section II, arise in this on-the-fly framework due to (i) the unknown quality of the retrieved examples and (ii) possible lack of source descriptions (i.e.…”