  In this paper, a novel approach for single channel source separation (SCSS)
using a deep neural network (DNN) architecture is introduced. Unlike previous
studies in which DNN and other classifiers were used for classifying
time-frequency bins to obtain hard masks for each source, we use the DNN to
classify estimated source spectra to check for their validity during
separation. In the training stage, the training data for the source signals are
used to train a DNN. In the separation stage, the trained DNN is utilized to
aid in estimation of each source in the mixed signal. Single channel source
separation problem is formulated as an energy minimization problem where each
source spectra estimate is encouraged to fit the trained DNN model and the
mixed signal spectrum is encouraged to be written as a weighted sum of the
estimated source spectra. The proposed approach works regardless of the energy
scale differences between the source signals in the training and separation
stages. Nonnegative matrix factorization (NMF) is used to initialize the DNN
estimate for each source. The experimental results show that using DNN
initialized by NMF for source separation improves the quality of the separated
signal compared with using NMF for source separation.
