In　this　paper,　we　propose　a　novel　speech　enhancement　method　using　multi-band　excitation　(MBE)　model.　MBE　model　is　a　famous　and　efficient　way　of　speech　coding.　Motivated　by　high　quality　of　its　synthetic　speech,　we　introduce　the　MBE　model　to　single-channel　speech　enhancement　system.　In　the　MBE　model,　the　entire　frequency　band　is　divided　into　several　sub-bands　and　each　sub-band　is　formed　as　voiced　or　unvoiced　speech.　In　order　to　reconstruct　speech,　there　are　three　acoustic　parameters　of　the　MBE　model　need　to　be　estimated,　including　pitch,　harmonic　magnitude　and　voiced/unvoiced　(V/UV)　decision　for　each　band.　To　calculate　the　parameters　accurately,　deep　neural　networks　(DNNs)　are　utilized　to　estimate　harmonic　magnitude　and　V/UV　decision.　In　order　to　learn　the　mapping　relationship　of　the　features　deeply,　different　types　of　noise　and　different　input　signal　to　noise　ratios　(SNRs)　of　noisy　speech　are　combined　to　form　a　big　training　set.　Another　parameter,　pitch,　is　calculated　from　the　pre-processed　speech　using　MBE　analysis　method.　Moreover,　speech　presence　probability　is　introduced　in　this　paper　to　remove　residual　noise　further.　Experimental　results　show　that　the　proposed　method　can　provide　higher　speech　quality　and　intelligibility　compared　with　some　reference　methods　to　some　extent.　(C)　2020　Elsevier　Ltd.　All　rights　reserved.