Beamforming　method　can　effectively　remove　background　noise,　even　in　the　complex　environment,　so　it　is　widely　used　in　speech　enhancement.　We　propose　a　novel　Generalized　Eigenvalue　(GEV)　beamforming　with　Blind　Analytic　Normalization　(BAN)　method.　In　this　method,　the　GEV　beamformer　coefficients　are　constructed　by　estimating　logarithmic　power　spectrum　(LPS),　which　are　used　to　filter　multichannel　speech　signals,　and　post　filter　technology　is　used　to　further　remove　noise　in　the　beamformed　signals.　Firstly,　in　order　to　estimate　the　LPS　of　speech　signal　in　each　channel,　we　use　the　data-driven　method　to　train　the　deep　neural　network　(DNN)　model.　Then,　we　use　the　well　trained　DNN　model　to　estimate　LPS,　which　is　used　to　calculate　the　power　spectral　density　(PSD)　matrix　of　speech,　and　further　obtain　the　coefficients　of　the　GEV　beamformer.　Since　the　GEV　beamformer　will　cause　speech　distortion,　the　BAN　is　employed　to　post-process　the　beamformed　signal.　Furthermore,　single　channel　speech　enhancement　is　used　to　reduce　residual　noise.　Our　experiment　is　conducted　in　8-channel　simulation　data　set.　The　experimental　results　show　that,　compared　with　some　existing　speech　enhancement　methods,　the　proposed　method　can　effectively　remove　background　noise　and　achieve　better　speech　enhancement　effect.　©　2020　IEEE.