  Research in deep learning for multi-speaker source separation has received a
boost in the last years. However, most studies are restricted to mixtures of a
specific number of speakers, called a specific scenario. While some works
included experiments for different scenarios, research towards combining data
of different scenarios or creating a single model for multiple scenarios have
been very rare. In this work it is shown that data of a specific scenario is
relevant for solving another scenario. Furthermore, it is concluded that a
single model, trained on different scenarios is capable of matching performance
of scenario specific models.
