Single-channel enhancement of convolutive noisy speech based on a discriminative NMF algorithm

In this paper, we introduce a discriminative training algorithm of the non-negative matrix factorization (NMF) model for single-channel enhancement of convolutive noisy speech. The basis vectors for the clean speech and noises are estimated simultaneously during the training stage by incorporating the concept of classification from machine learning. Specifically, we employ the probabilistic generative model (PGM) of classification, specified by an inverse Gaussian distribution, as a priori structure for the basis vectors. Both the NMF and classification parameters are obtained by using the expectation-maximization (EM) algorithm, which guarantees convergence to a stationary point. Experimental results show that the proposed algorithm provides better enhancement performance than the benchmark algorithms.