由於傳統圓型麥克風陣列不能進行仰角的定位，對仰角不敏感，且傳統垂直式線性麥克風陣列則是只能進行仰角定位，對水平角不敏感。本論文提出增強型的環形麥克風陣列，將一個對數間距的線性陣列垂直放置在圓型麥克風陣列的中心點上進行三維聲場之定位並和球型麥克風陣列來比較，其中圓型麥克風陣列架構為24個等間距的麥克風散佈在圓環上，對數間距的線性陣列架構為8個對數間距的麥克風垂直分布在圓環中心點上，而球型麥克風陣列為32個麥克風根據特定位置分布。定位階段利用最小變易無失真響應法、多重信號分類法與正交匹配追踪法來進行聲源定位，三個方法都有不錯的定位效果。定位結果以演算法來區分的話，正交匹配追蹤法運用的時間最久。分離階段運用梯克諾夫正規化法、有效的凸集合最佳化及正交匹配追踪法來進行分離。分離結果以有效的凸集合最佳化及正交匹配追踪法有較好的分離度，但失真較為嚴重，梯克諾夫正規化法則相反。定位及分離以模型來比較的話，球形麥克風陣列分析時間高出增強型的環形麥克風陣列。最後將分離出來的音檔配合定到位置的聲源角度利用頭部轉移函數轉換成左右聲道輸入耳機，使得聽覺上能夠有方向性。配合聆聽測試的結果，均有不錯的方向性。

A conventional uniform circular microphone array (UCA) cannot separate the signal in the zenith angle, whereas a vertical uniform circular microphone array (ULA) cannot extract the signals in the azimuth. This thesis presents an augmented circular microphone array (CMA) consisting of an unbaffled CMA with a logarithmic-spacing linear array (LLA) as the vertical extension to analyze the 3-D sound field. A spherical microphone array (SMA) consisting of 32 microphones is utilized as a benchmarking method for the augmented CMA above. However the CMA architecture has 24 equally spaced microphones mounted on the surface and LLA architecture has 8 logarithmically spaced microphones perpendicularly standing above the center of the CMA. The methods of localization use minimum power distortionless response (MPDR), multiple signal classification (MUSIC) and orthogonal matching pursuit (OMP). All of them have a good effect of localization. The OMP takes the longest time for localization. The methods of separation use Tikhonov regularization (TIKR), Convex optimization (CVX) and OMP. The separation results of CVX and OMP have better performance, but the distortion is more serious. However, the TIKR is the opposite result. Finally, by using the head related transfer functions (HRTF), the source signals are converted into the left and right channels for the headset. The results of the listening test have demonstrated the efficacy of binaural rendering by using the proposed system.

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