Presently,　because　of　the　development　of　deep　learning　technology,　there　has　been　increasingly　more　attention　on　state-of-The-Art　masking　and　mapping　based　speech　enhancement　methods.　However,　traditional　speech　enhancement　approaches,　like　minimum　mean-square　error　(MMSE)　and　wiener　filter　(WF)　have　not　been　fully　investigated.　In　order　to　the　better　characterize,　we　proposed　a　deep　learning　based　MMSE　approach　for　single-channel　speech　enhancement　based　on　Non-negative　Matrix　Factorization　(NMF).　The　performance　of　MMSE　approach　can　be　improved　by　a　priori　signal-To-noise　ratio.　Therefore,　we　utilized　an　NMF-based　Densely　Connected　Convolutional　Network　(DenseNet)　as　an　estimator　of　the　a　priori　signal-To-noise　ratio　(SNR).　In　test　stage,　multiple　SNR　level　speech　from　colored　noise　sources　and　real-world　non-stationary　noise　sources　were　used　for　evaluation.　As　expected,　our　present　study　outperformed　many　previous　speech　enhancement　methods.　©　2021　IEEE.