Sound　source　separation　is　a　hot　topic　in　array　signal　processing.　In　fact,　it　is　difficult　to　separate　multiple　sound　sources　in　reverberant　environments.　To　solve　this　problem,　a　two-stage　multiple　sound　source　separation　method　is　proposed　based　on　the　fully-convolutional　time-domain　audio　separation　network　(Conv-TasNet)　and　deep　neural　network　(DNN).　In　the　first　stage,　the　end-to-end　separation　network　(i.e.,　the　Conv-TasNet)　is　employed　to　separate　the　sound　sources　from　the　signal　recorded　in　reverberant　environments.　We　use　the　encoder　to　generate　the　time　domain　representation　of　the　recorded　signal,　and　the　mask　is　obtained　by　the　Conv-TasNet　model.　The　recovery　of　each　separated　sound　source　signal　is　conducted　by　multiplying　the　output　of　the　encoder　by　the　mask.　In　the　second　stage,　the　separated　signal　is　enhanced　with　the　single　DNN.　The　training　target　of　the　DNN　is　to　obtain　the　ideal　enhancement　mask,　which　is　calculated　by　integrating　the　amplitude　of　the　frequency　domain　coefficients　of　both　separated　signal　and　clean　signal.　The　amplitude　of　the　frequency　domain　coefficients　of　the　separated　signals　is　used　as　the　input　of　DNN　in　order　to　predict　the　ideal　ratio　mask　(IRM).　IRM　is　multiplied　by　the　original　amplitude　to　obtain　the　enhanced　amplitude　of　the　frequency　domain　coefficients,　which　is　integrated　with　phase　to　obtain　the　enhanced　source　signal.　The　results　of　the　subjective　and　objective　evaluation　show　that,　compared　with　the　reference　methods,　the　proposed　method　achieves　better　separation　quality　in　both　reverberant　and　anechoic　acoustic　environments.　©　2021　IEEE.