Abstract: This paper presents a two stages speech dereverberation method which combine the bidirectional Long Short Term Memory (BLSTM) recurrent neural network with non-negative matrix factorization (NMF) for a single channel. The log power spectra is selected as features to suppress the reverberation. The BLSTM-RNN which can capture information from anywhere in the feature sequence is used to dereverberated log power spectra firstly and NMF which could alleviate the over-smoothing problem is applied to generated log power spectra in the second stage. Experimental results demonstrate that the proposed method could achieve significant improvements over the different baseline methods.