在近年來的飛安課題上，有關聲音訊號之分離與辨識為一項座艙通話紀錄器(Cockpit Voice Recorder, CVR)分析之最新運用，此項新技術並已成功地運用在數起飛安事故之調查上。而鑒於飛機座艙內之聲音普遍具有位置固定之特性，若能將駕駛員語音或開關撥動聲響等發音位置加以定位，則更有助於協助調查人員判別飛行人員於失事前關鍵時刻之操作狀況，作為失事調查之佐證。本研究乃模仿生物對於聲音定位之原理，利用音源行進至不同受音器官間所造成的時間差(Time Delay of Arrival, TDOA)，找出可能的位置，達成音源定位之目的。在本研究中所述之TDOA方法，其計算流程主要可分為兩步驟：首先；量測並計算單一音源抵達一組不同麥克風時所造成的時間差，其次；再利用此一量得的時間差反推回音源當時之發音位置。

　　本研究中，首先利用交相關(Cross-correlation)法則來求得聲音抵達艙內麥克風組時之時間差異，並再分別利用線性相交(Linear-Intersection, LI)及最佳化(Optimization)兩種不同之演算方法於駕駛艙內音源之定位推算。整套理論與計算過程在MD-80噴射客機實機駕駛艙內進行多次實際驗證，所得的結果可作為此一新方法運用於失事調查之可行性評估依據。本研究於多次之實機實驗後，驗證此一方法目前可達成準確地判別並定位出正/副駕駛之語音訊號，並在文末提出麥克風配置方式對於音源定位精度之影響，且簡述未來可能提昇定位精度之更進一步研究。

　　Identifying the sound sources from the aircraft’s cockpit panel and the pilot is a new flight safety topic, analyzing the voice data from a Cockpit Voice Recorder (CVR) after the aircraft accident. Since most of the characters that all sound sources generated are fixed in the cockpit, their relevant positions can be served as an evident parameter in differentiating distinct sound sources in the cockpit environment. A theory named source localization is widely used in positioning these sound sources. Inspired from the animal ability of hearing, the method, called Time Delay of Arrival (TDOA), is introduced and capable of executing in the present study. The TODA technique includes two-step process as: the first is to estimate the time-delay between a pair of microphones, the second is then to determine the relative position of the expected sound sources via the time-delay.
　　To estimate the time-delay between the microphone pair, the cross-correlation is calculated in this thesis. After acquiring the time-delay data, two distinct search algorithms, namely Linear Intersection (LI) and Optimization, are utilized. These two algorithms are specially validated by conducting the experiments of recording the sound data in the real cockpit of an aircraft MD-80. The results obtained from several real experiments are presented to illustrate the adequacy of the utilized source localization techniques in this thesis. Finally, it has successfully located the pilot-copilot’s pronunciation accordingly recorded in CVR, while further analysis to investigate a well-arrangement of microphones can enhance the accuracy.

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