The　goal　of　speech　separation　is　to　separate　the　target　signal　from　the　background　interference.　With　the　rapid　development　of　artificial　intelligence,　speech　separation　technology　combined　with　deep　learning　has　received　more　attention　as　well　as　a　lot　of　progress.　However,　in　the　“cocktail　party　problem”,　it　is　still　a　challenge　to　achieve　speech　separation　under　reverberant　conditions.　In　order　to　solve　this　problem,　a　model　combining　the　Weighted　Prediction　Error　(WPE)　method　and　a　fully-convolutional　time-domain　audio　separation　network　(Conv-Tasnet)　is　proposed　in　this　paper.　The　model　target　on　separating　multi-channel　signals　after　dereverberation　without　prior　knowledge　of　the　second　field　environment.　Subjective　and　objective　evaluation　results　show　that　the　proposed　method　outperforms　existing　methods　in　the　speech　separation　tasks　in　reverberant　and　anechoic　environments.　©　2023　by　the　authors.