In　this　paper,　a　multi-channel　speech　enhancement　method　with　the　minimum　variance　distortionless　response　(MVDR)　beamforming　method　based　on　the　time-frequency　(T-F)　masking　is　proposed.　In　this　study,　First,　the　logarithmic　power　spectrum　(LPS)　features　of　multichannel　signals　are　used　as　input　features　to　estimate　a　T-F　mask　of　the　reference　microphone　by　the　deep　neural　network　(DNN)　model.　Then,　the　estimated　mask　is　utilized　to　calculate　speech　covariance　matrix　that　is　used　to　estimate　a　steering　vector　for　constructing　the　MVDR　beamformer.　The　steering　vector　is　estimated　by　the　generalized　eigenvalue　decomposition　(GEVD)　method.　Finally,　the　output　speech　of　the　beamformer　is　processed　by　the　DNN-based　IRM　model.　In　order　to　prove　the　effectiveness　of　the　proposed　method,　the　perceptual　evaluation　of　speech　quality　(PESQ)　and　the　segment　signal-to-noise　ratio　(SSNR)　are　employed.　The　experimental　results　show　that　the　proposed　method　effectively　increased　the　PESQ　and　SSNR.　©　2019　IEEE.