  We propose an online estimated dictionary based single channel speech
enhancement algorithm, which focuses on low rank and sparse matrix
decomposition. In this proposed algorithm, a noisy speech spectral matrix is
considered as the summation of low rank background noise components and an
activation of the online speech dictionary, on which both low rank and sparsity
constraints are imposed. This decomposition takes the advantage of local
estimated dictionary high expressiveness on speech components. The local
dictionary can be obtained through estimating the speech presence probability
by applying Expectation Maximal algorithm, in which a generalized Gamma prior
for speech magnitude spectrum is used. The evaluation results show that the
proposed algorithm achieves significant improvements when compared to four
other speech enhancement algorithms.
