Discriminative Training of NMF Model Based on Class Probabilities for Speech Enhancement
Hanwook Chung, Éric Plourde, Benoı̂t Champagne · IEEE Signal Processing Letters · 2016
In this letter, we introduce a discriminative training algorithm of the basis vectors in the nonnegative matrix factorization (NMF) model for single-channel speech enhancement. 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 consider the probabilistic generative model (PGM) of classification, which is specified by class-conditional densities, along with the NMF model. The update rules of the NMF are jointly obtained with the parameters of the class-conditional densities using the expectation-maximization (EM) algorithm, which guarantees convergence. Experimental results show that the proposed algorithm provides better performance in speech enhancement than the benchmark algorithms.