本論文主要探討摺積混合的盲訊號分離演算法，希望能解決實際環境下，語音訊號處理中所描述的雞尾酒會問題。論文採用的基本盲訊號分離演算法為聯合近似對角化演算法，應用此方法解決盲訊號分離問題時並不用對混合訊號做集中化和白色化的前處理，能避免訊號因前處理而造成統計特性的改變，因而有不錯的分離效果。為了改進計算速度緩慢的缺點，首先利用人耳的聽覺特性，以巴克刻度為基準，對低頻部份以較密集的頻率間隔做盲訊號分離的演算，高頻部份頻率間隔則較稀疏，以符合人耳聽覺上對訊號低頻成份變動較敏感，高頻成份較不敏感的特性。結果顯示單純使用此方法可以讓演算時間縮減54%。接著利用聯合近似對角化演算法的特性，由每次迭代過程得到的矩陣去預估下一次迭代過程可能得到的矩陣，藉此加速演算法收斂，結果顯示使用此方法可以讓演算時間縮減42%。實際運算時先將訊號轉至頻域，接著計算訊號的交頻譜當成要聯合對角化的目標矩陣。利用改良的加速演算法對各別的離散頻率進行盲訊號分離；在解決排列和膨脹問題後估得各離散頻率的解混合矩陣，並求得時域的分離訊號。實驗時嘗試將兩個麥克風在實際環境中錄到的雙人混合語音分離開，結果顯示可以減少71%的計算時間，並且訊號分離的成果也和加速前相差不遠。

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