This　paper　provides　a　novel　deep　neural　networks　(DNN)　based　speech　enhancement　method　using　multi-band　excitation　(MBE)　model.　Generally,　the　proposed　system　contains　two　stages,　namely　training　stage　and　enhancing　stage.　In　the　training　stage,　two　DNNs　with　different　targets　are　trained.　The　training　targets　are　harmonic　magnitude　and　band　difference　function　of　clean　speech,　respectively.　The　input　feature　for　two　DNNs　is　log-power　spectra　(LPS)　of　noisy　speech.　In　the　enhancing　stage,　using　the　output　of　DNNs　and　online　estimated　pitch　period,　the　enhanced　speech　can　be　obtained　by　MBE　speech　synthesis.　Using　the　proposed　method,　the　parameters　of　MBE　model　can　be　accurately　estimated　to　synthesize　the　enhanced　speech　with　the　high　quality.　At　the　same　time,　the　noise　between　the　harmonics　is　effectively　eliminated.　The　experiments　show　that　the　proposed　method　outperforms　the　reference　methods　for　speech　quality　and　intelligibility.　©　2018　IEEE.