Nonnegative matrix or tensor factorization is a very popular approach for audio source separation. One important problem in nonnegative tensor factorization (NTF) in the context of user-guided audio source separation is the necessity to manually assign the NTF components to audio sources in order to be able to enforce prior information on the sources during the estimation process. In this paper, two new approaches to NTF based source separation are proposed , which do not require any manual component assignment to the sources, but estimate the underlying assignment automatically. Both algorithms use the prior information on the source samples in the estimation process along with either a limit on the minimum number of components each source uses or with a restriction that each component is used by sparse number of sources. The proposed methods are shown to outperform the classic approach with a manual distribution of the components equally among the sources.