Deep　neural　network　(DNN)　based　ideal　ratio　mask　(IRM)　estimation　methods　have　yielded　good　performance　in　monaural　speech　enhancement.　Meanwhile,　these　methods　have　also　shown　considerable　potential　for　beamforming　and　multichannel　speech　enhancement.　It　is　crucial　for　minimum　variance　distortionless　response　(MVDR)　beamformer　to　estimate　the　covariance　matrix　of　the　speech　and　noise　accurately.　The　accurate　estimation　of　time-frequency　(T-F)　mask　has　significant　impact　on　the　estimation　of　the　covariance　matrices.　So,　in　this　paper,　a　complex　real　and　imaginary　ratio　mask　(CRIRM)　based　MVDR　beamformer　for　speech　enhancement　using　residual　network　is　proposed.　First,　the　real　and　imaginary　masks　of　speech　and　noise　are　estimated　by　taking　advantage　of　a　residual　neural　network.　After　that,　the　estimations　of　speech　and　noise　are　obtained　by　using　the　estimated　masks.　Finally,　the　covariance　matrices　of　speech　and　noise　are　estimated,　and　applied　into　the　MVDR　beamformer.　In　addition,　in　order　to　further　reduce　residual　noise　interference,　the　output　of　the　MVDR　beamformer　is　further　processed　by　an　end-to-end　monaural　speech　enhancement　module.　Experiments　show　that,　the　proposed　method　can　better　improve　the　quality　and　intelligibility　of　the　enhanced　speech.