Discriminative NMF and its application to single-channel source separation

The objective of single-channel source separation is to accurately recover source signals from mixtures. Non-negative matrix fac-torization (NMF) is a popular approach for this task, yet previous NMF approaches have not optimized directly this objective, de-spite some efforts in this direction. Our paper introduces discrim-inative training of the NMF basis functions such that, given the coefficients obtained on a mixture, a desired source is optimally recovered. We approach this optimization by generalizing the model to have separate analysis and reconstruction basis func-tions. This generalization frees us to optimize reconstruction ob-jectives that incorporate the filtering step and SNR performance criteria. A novel multiplicative update algorithm is presented for the optimization of the reconstruction basis functions accord-ing to the proposed discriminative objective functions. Results