The　performance　of　the　existing　speech　enhancement　algorithms　is　not　ideal　in　low　signal-to-noise　ratio　(SNR)　non-stationary　noise　environments.In　order　to　resolve　this　problem,　a　novel　speech　enhancement　algorithm　was　presented.First,　a　fully　connected　deep　neural　network　(DNN)　was　constructed,　and　a　multi-resolution　auditory　cepstral　coefficient　(MRACC)　was　extracted　from　four　cochleagrams　of　different　resolutions　as　the　input　of　neural　network,　which　could　capture　the　local　information　and　spectrotemporal　context.Second,　an　adaptive　mask　(AM)　which　can　adjust　the　weight　of　ideal　binary　mask　(IBM)　and　ideal　ratio　mask　(IRM)　according　to　noise　change　was　put　forward　in　this　paper.Finally,　the　estimated　AM　was　used　to　achieve　the　enhanced　speech.The　proposed　algorithm　shows　that　it　not　only　further　improves　speech　quality　and　intelligibility,　but　also　suppresses　more　noise　than　the　contrast　algorithms　by　experimental　results.　©　2019,　Editorial　Board　of　Journal　of　Huazhong　University　of　Science　and　Technology.　All　right　reserved.