This　paper　proposes　a　novel　localization　scheme　for　multiple　sound　sources　that　imposes　the　relaxed　sparsity　constrains　(not　all　time-frequency　coefficients　are　overlapped)　on　the　source　signals.　First,　a　＂DOA　convergence＂　assumption　is　proposed,　which　means　that　if　most　of　the　time-frequency　(T-F)　bins　in　a　T-F　zone　are　derived　from　only　one　source-　defined　as　single　source　bins　(SSBs),　the　corresponding　direction　of　arrival　(DOA)　estimates　are　relatively　concentrated　with　a　heavy　density.　This　assumption　is　validated　through　statistical　analysis　by　applying　a　quantitative　measure　of　convergence.　Accordingly,　by　applying　the　＂DOA　convergence＂　assumption,　the　detection　of　SSBs　is　converted　to　a　clustering　problem,　K-means　clustering　and　density-based　spatial　clustering　of　applications　with　noise　(DBSCAN)　algorithms　are　utilized　to　complete　the　task　in　this　paper.　The　cross　distortions　(localization　error　due　to　the　cocktail　party　phenomenon)　in　localization　caused　by　multiple　simultaneously　occurring　sources　is　significantly　weakened　by　conducting　DOA　estimation　among　these　SSBs,　i.e.,　the　multiple　source　localization　is　rewritten　to　a　single　source　one　among　these　SSBs.　Moreover,　the　proposed　SSBs　detection　is　applicable　to　other　localization　methods　and　not　limited　to　specific　microphone　topology.　Experimental　results　demonstrate　the　localization　accuracy　of　the　proposed　method　outperforms　the　state-of-the-　art　localization　approaches　which　are　based　on　single　source　zone　detection.　However　the　proposed　method　is　capable　of　realtime　processing,　the　accuracy　is　insufficient　in　the　current　system.　If　non-real　time　processing　is　allowed,　our　method　can　be　realized　with　higher　accuracy　than　the　conventional　ones.