近年來越來越多的研究從事聲訊與視訊的結合來做聲源定位，可以減低單一使用聲訊在充滿雜訊以及聲音迴響的環境下估計聲源方位所造成的誤差。本論文就是以兩支麥克風與筆記型電腦上的網路攝影機針對說話者做聲源定位。在聲訊方面是利用雙曲線的定義估計聲源的角度。在視訊方面是利用Viola與Jones提出的人臉偵測演算法偵測到人臉之後，再利用Turk與Pentland提出利用主成份分析法(Principal Component Analysis, PCA)找到每個人不同的eigenface來做人臉辨識。
因此本論文的系統架構是先利用視訊偵測到人臉在影像中的大小估計人到網路攝影機的垂直距離，在結合利用雙曲線定義所估計出的聲源角度，求得當聲源是人時，以網路攝影機(即兩支麥克風的中點)為中心的二維平面座標。本論文除了可以偵測說話者的方位以及辨識說話者的身分之外，同時也利用聲源角度的資訊輔助影像針對人臉旋轉偵測。並且在假設聲源之間彼此的訊號不相關(uncorrelated)時，可以利用視訊偵測到的人臉個數與聲訊利用交互相關函數來估計在室內環境下的潛在聲源個數。
本研究方法，實驗量測結果得知:在利用視訊結合聲訊針對人做聲源定位的二維平面座標誤差不超過5cm。並且假設聲源之間彼此的訊號不相關以及筆記型電腦上的網路攝影機視角範圍限制在-25°~25°之下，只要利用兩支麥克風就可以偵測到兩個聲源同時發聲。

Much research has been investigated regarding the source detection by joining audio and video methods recently. The audio-video method performs better in bias reduction for source detection in the noisy and reverberant environment than using the audio method alone. In this thesis, we design a system for talker detection by using two microphones and the web camera. For audio, we use the definition of hyperbolic surface to estimate the direction of sound sources relative to the microphones. For video, we use Viola-Jones algorithm to detect the face. Afterwards, we use Turk-Pentland algorithm to find the eigenface by principal component analysis, and later use the eigenface to recognize the face.
The location of a talking person is determined in two steps. First, we estimate the normal distance between the talker and the imaging plane of the camera by the size of the talker’s face in the image. Then, an estimate of two-dimensional location of the talker is obtained by considering the angle of the talker relative to the camera (or the center of two microphones). Because of using video and audio information jointly, the system can identify the talker, and face detection can be made robust against rotations thanks to the availability of audio information. In addition, when there are multiple talkers in the room, the number of sound sources can be estimated under the assumption that the sources are uncorrelated; this can be achieved either by counting the number of faces in video or calculating the cross correlation function between signals obtained by two microphones.
Experiments were conducted and results showed that the bias for estimating the location of a single talker is less than 5cm. Experiments for double talker estimation were also conducted, and we demonstrated that, in principle, we can only use two microphones to detect two sources as long as that they are uncorrelated.

第一章 緒論 1
1.1 研究動機 1
1.2 研究方法 2
1.3 系統架構 5
1.4 章節概要 6
第二章 聲源定位 7
2.1聲源為平面波的成立條件 8
2.2交叉相關函數(CROSS CORRELATION FUNCTION) 9
2.3利用雙曲線平面定義估計聲源的方位 20
第三章 人臉偵測 22
3.1 HAAR-LIKE特徵(HAAR-LIKE FEATURE) 22
3.2 積分影像(INTEGRAL IMAGE) 25
3.3 人臉距離估計 31
第四章 人臉辨識 35
4.1 EIGENFACE 35
4.2 利用EIGENFACES辨識人臉影像 39
第五章 聲源與影像資訊偵測說話者應用情境 41
5.1 影像和實際物體移動比例計算 43
5.2 利用聲源角度與人臉影像針對人臉旋轉偵測 43
第六章 實驗裝置與系統介面操作流程 45
6.1 實驗裝置 45
6.2 系統介面介紹與操作流程 49
第七章 實驗結果與討論 54
實驗1: 54
系統偵測不同聲源種類的角度誤差以及標準差的分析與比較。 54
實驗2: 58
說話者站在三個不同的位置發聲，並觀察放置三個不同位置所估計以兩支麥克風為中心的二為平面座標。 58
實驗3: 60
使用兩支喇叭撥放沒有相關(UNCORRELATED)的聲源訊號，並觀察放置在不同位置的角度最大交互相關係數所對應的延遲位置。 60
第八章 結論與未來展望 66
8.1 結論 66
8.2 未來展望 67
REFERENCE 68
 

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