Deep　neural　network　(DNN)　has　become　a　popular　means　for　separating　target　speech　from　noisy　speech　in　the　supervised　speech　enhancement　due　to　its　good　performance　for　learning　higher-level　information.　For　DNN-based　methods,　the　training　target　and　acoustic　features　have　a　significant　impact　on　the　performance　of　speech　restoration.　The　ideal　ratio　mask　(IRM)　is　commonly　used　as　the　training　target.　But,　generally　it　does　not　take　into　account　phase　information.　The　recent　studies　have　revealed　that　incorporating　phase　information　into　the　mask　can　effectively　help　improve　speech　quality　of　the　enhanced　speech.　In　this　paper,　a　bounded　IRM　with　phase　parameterization　is　presented　and　used　as　the　training　target　of　the　DNN　model.　In　addition,　some　acoustic　features　with　harmonic　preservation　are　incorporated　into　the　input　of　DNN　model,　which　are　considered　as　additional　information　to　improve　quality　of　the　enhanced　speech.　The　experiments　are　performed　under　various　noise　environments　and　signal　to　noise　ratio　(SNR)　conditions.　The　results　show　that　the　proposed　method　can　outperform　reference　methods.　©　2019　IEEE.