In　order　to　improve　the　performance　of　speech　enhancement　algorithm　in　low　Signal-to-Noise　Ratio　(SNR)　complex　noise　environments,　a　novel　Improved　Least　Mean　Square　Adaptive　Filtering　(ILMSAF)　based　speech　enhancement　algorithm　with　Deep　Neural　Network　(DNN)　and　noise　classification　is　proposed.　An　adaptive　coefficient　of　filter＇s　parameters　is　introduced　into　conventional　Least　Mean　Square　Adaptive　Filtering　(LMSAF).　First,　the　adaptive　coefficient　of　filter＇s　parameters　is　estimated　by　Deep　Belief　Network　(DBN).　Then,　the　enhanced　speech　is　obtained　by　ILMSAF.　In　addition,　in　order　to　make　the　presented　approach　suitable　for　various　kinds　of　noise　environments,　a　new　noise　classification　method　based　on　DNN　is　presented.　According　to　the　result　of　noise　classification,　the　corresponding　ILMSAF　model　is　selected　in　the　enhancement　process.　The　performance　test　results　under　ITU-TG.160　show　that,　the　performance　of　the　proposed　algorithm　tends　to　achieve　significant　improvements　in　terms　of　various　speech　subjective　and　objective　quality　measures　than　the　wiener　filtering　based　speech　enhancement　approach　with　Weighted　Denoising　Auto-encoder　and　noise　classification.　(C)　2016　Elsevier　B.V.　All　rights　reserved.