This　article　aims　to　address　the　multi-source　localization　problem　by　exploiting　the　sparsity　of　the　speech　signal　in　the　time-frequency　domain,　where　the　challenge　mainly　lies　in　extracting　the　sparse　component.　An　optimized　time-frequency　representation　and　sparsity　component　analysis-based　multi-source　localization　method　is　proposed　to　overcome　this　challenge.　Firstly,　extracting　the　sparse　components　relies　on　the　accurate　representation　in　the　time-frequency　domain.　However,　the　energy　leakage　problem　caused　by　linear　time-frequency　transformation　limits　the　accuracy　of　sparse　component　extraction.　To　tackle　this　problem,　inspired　by　empirical　mode　decomposition,　the　proposed　method　classifies　all　the　points　in　the　time-frequency　domain　into　four　categories　based　on　their　phase　feature　and　mode　characteristics.　Each　type　of　the　point　is　modeled　separately,　and　a　point-by-point　analysis　is　conducted　to　remove　all　the　points　affected　by　energy　leakage.　Then,　based　on　the　optimized　time-frequency　representation,　the　phase　coherence　criterion　is　used　to　detect　the　sparse　component　in　the　point　level.　Following　that,　guided　by　the　mode　consistency　characteristic　of　sparse　components,　an　extension　scheme　is　proposed　to　recover　the　falsely　removed　sparse　components.　Finally,　the　detected　sparse　components　are　applied　for　the　multiple　source　localization.　The　objective　evaluation　is　performed　in　both　simulation　and　actual　recording　environments,　and　the　proposed　method　can　achieve　better　localization　accuracy　compared　to　several　existing　methods.