This　paper　proposes　a　blind　source　separation　(BSS)　method　for　recovering　multiple　speech　sources　from　sound　fields　recorded　by　a　B-format　microphone.　This　microphone　provides　a　four　channel　representation　that　can　be　used　to　derive　the　direction　of　arrival　(DOA)　of　spatially　distinct　time-frequency　(TF)　components.　Such　sparse　components　correspond　to　bins　where　only　one　speech　source　is　active　and　are　identified　based　on　the　inter　correlation　among　the　mixture　signals.　They　are　recovered　via　a　degenerate　unmixing　estimation　technique　(DUET)-like　method.　Proposed　is　a　＂local-zone　stationarity＂　assumption,　where　the　amplitude　of　a　speech　signal　remains　approximately　constant　within　a　small　band　of　TF　components.　This　assumption　is　validated　through　statistical　analysis　of　a　quantitative　measure　of　stationarity.　Under　this　assumption,　the　non-sparse　components　(TF　points　where　more　than　one　speech　source　is　active)　are　recovered　via　a　Wiener-filter-like　approach　where　the　separated　sparse　components　is　utilized　as　a　guide.　The　final　separated　sources　are　obtained　by　combining　the　separated　sparse　and　non-sparse　components.　Both　objective　and　subjective　evaluations　show　that　the　proposed　method　achieves　better　separation　quality　compared　to　some　existing　BSS　approaches　where　up　to　six　simultaneous　speech　sources　are　considered.