A Neural Network Alternative to Non-Negative Audio Models
  We present a neural network that can act as an equivalent to a Non-Negative Matrix Factorization (NMF), and further show how it can be used to perform supervised source separation. Due to the extensibility of this approach we show how we can achieve better source separation performance as compared to NMF-based methods, and propose a variety of derivative architectures that can be used for further improvements. 
Paris Smaragdis, Shrikant Venkataramani
cs.SD