  Discriminative models for source separation have recently been shown to
produce impressive results. However, when operating on sources outside of the
training set, these models can not perform as well and are cumbersome to
update. Classical methods like Non-negative Matrix Factorization (NMF) provide
modular approaches to source separation that can be easily updated to adapt to
new mixture scenarios. In this paper, we generalize NMF to develop end-to-end
non-negative auto-encoders and demonstrate how they can be used for source
separation. Our experiments indicate that these models deliver comparable
separation performance to discriminative approaches, while retaining the
modularity of NMF and the modeling flexibility of neural networks.
