Multiple　speech　source　separation　plays　an　important　role　in　many　applications　such　as　automatic　speech　recognition,　acoustical　surveillance,　and　teleconferencing.　In　this　study,　we　propose　a　method　for　the　separation　of　multiple　speech　sources　in　a　reverberant　environment　based　on　sparse　component　enhancement.　In　a　recorded　signal　(i.e.,　a　mixed　signal　of　multiple　speech　sources),　there　are　always　time-frequency　points　where　only　one　source　is　active　or　dominant.　It　is　the　sparsity　of　speech　signals.　Such　time-frequency　points　are　called　sparse　component　points.　However,　in　a　reverberant　environment,　the　sparsity　of　the　speech　signal　is　affected,　resulting　in　a　decrease　in　the　number　of　sparse　component　points　in　the　recorded　signal,　which　affects　the　quality　of　the　separated　source　signal.　In　this　study,　for　mixture　signals　recorded　by　a　soundfield　microphone　(a　microphone　array),　we　first　experimentally　analyze　the　negative　impact　of　reverberation　on　sparse　components　and　then　develop　a　sparse　component　enhancement　method　to　increase　the　number　of　these　points.　Then,　the　sparse　components　are　identified　and　classified　according　to　the　directions　of　arrival　estimate　of　the　sources.　Next,　the　sparse　components　are　used　to　guide　the　recovery　of　the　non-sparse　components.　Finally,　multiple　source　separation　is　achieved　by　the　joint　restoration　of　the　sparse　and　non-sparse　components　of　each　source.　The　proposed　method　has　low　computational　complexity　and　applies　to　underdetermined　scenarios.　Through　a　series　of　subjective　and　objective　evaluation　experiments,　the　effectiveness　of　the　method　is　verified.