A　novel　speech　enhancement　method　based　on　Weighted　Denoising　Auto-encoder　(WDA)　and　noise　classification　is　proposed　in　this　paper.　A　weighted　reconstruction　loss　function　is　introduced　into　the　conventional　Denoising　Auto-encoder　(DA),　and　the　relationship　between　the　power　spectra　of　clean　speech　and　noisy　observation　is　described　by　WDA　model.　First,　the　sub-band　power　spectrum　of　clean　speech　is　estimated　by　WDA　model　from　the　noisy　observation.　Then,　the　a　priori　SNR　is　estimated　by　the　a　Posteriori　SNR　Controlled　Recursive　Averaging　(PCRA)　approach.　Finally,　the　clean　speech　is　obtained　by　Wiener　filter　in　frequency　domain.　In　addition,　in　order　to　make　the　proposed　method　suitable　for　various　kinds　of　noise　conditions,　a　Gaussian　Mixture　Model　(GMM)　based　noise　classification　method　is　employed.　And　the　corresponding　WDA　model　is　used　in　the　enhancement　process.　From　the　test　results　under　ITU-T　G.160,　it　is　shown　that,　in　comparison　with　the　reference　method　which　is　the　Wiener　filtering　method　with　decision-directed　approach　for　SNR　estimation,　the　WDA-based　speech　enhancement　methods　could　achieve　better　objective　speech　quality,　no　matter　whether　the　noise　conditions　are　included　in　the　training　set　or　not.　And　the　similar　amount　of　noise　reduction　and　SNR　improvement　can　be　obtained　with　smaller　distortion　on　speech　level.　(C)　2014　Elsevier　B.V.　All　rights　reserved.