本論文選擇在頻域上執行盲訊號分離，其系統具有較佳的效率。在頻域計算的優勢為可以在每個頻率柱（frequency bin）分離且獨立，但其代價為存在膨脹（scale ambiguity）和排列（permutation ambiguity）的不確定性。其不明確原因為本篇論文主要探討重點。運用獨立成份分析演算法達成盲訊號分離，使用二階統計量來分離混合訊號，導致了聯合對角化問題。採用聯合近似對角化演算法處理盲訊號分離問題時，混合訊號不必經過白色化和集中化的過程，可減少訊號在進行分離前因作前置處理而失真的可能性。由於頻域摺積混合的方式會存在膨脹及排列問題，因此本論文利用訊號包絡線（envelope）結構增加頻域的連結，並且應用分離矩陣和訊號相鄰頻帶的相關性解決上述不明確問題。實驗用三支麥克風在真實環境中錄製分別由三個喇叭播放的音檔，利用論文架構從混合聲音中分離出各自的音檔。結果證實於真實環境下多個訊號源的分離，其平均訊號干擾率比值有明顯進步，理論分析和實驗的結果都顯示了該方法的有效性，代表此方法可有效提升盲訊號分離的結果。

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