聲音訊號至今為止無論是在多媒體或是通訊領域中都扮演著重要的角
色，而如何增強聲音訊號免除雜訊的干擾一直都是通訊產品關心的重要
議題。在聲音波束形成中最常被討論的兩個方法為：1. Delay and Sum 2.Minimum Variance Distortionless Response (MVDR). 本論文在室內空間使用Raspberry Pi 簡易的開發平台並搭配多支麥克風來收集聲音訊號以建立聲音波束形成的系統，實現以上兩種方法。其中在同步訊號中，time difference of arrival (TDOA) 的部分將帶入Fracitonal Delay 的概念以提高準確度，另外本論文提出在時域修正權重讓雜訊能量統一的方法提升訊號增強的效果。聲音訊號在不同麥克風中有不同能量的雜訊干擾，本論文假設這些雜訊都具有隨機且彼此皆無相關性的特性，而所有訊號之間在雜訊能量相等時，可以達較好的訊號增強效果。經過實驗，在多個SNR 彼此相異的訊號中，本論文所提出的權重修正方法相較於傳統方法使聲音訊號提高更多的訊噪比(SNR)，成功降低訊號噪音並改善室內聲音訊號的品質。

Acoustic signals play an important role in both multimedia and communication,and acoustic signal enhancement using beamforming is still a challenging issue, especially for communication equipments. Two main methods are frequently used in acoustic beamforming : 1. Delay and Sum 2. Minimum Variance Distortionless Response (MVDR). In this research, we intend to construct an acoustic beamforming system to implement the two methods in the indoor environment by using multiple microphones and a low-cost single board computer, Raspberry Pi. For the estimation of time difference of arrival (TDOA), fractional delay is calculated to increase the resolution in time. In order to improve the acoustic enhancement, we propose a method that determines weights to make the power of noise leveled across different channels. We assume the noise is uncorrelated across channels, so the signalto-noise ratio can be improved simply by summation if signals are accurately aligned in time. The experiment result shows that the proposed method of determining the weights successfully enhances the signal in the indoor environmentand increases signal to noise ratio (SNR) in comparison with the conventional Delay and Sum method .

1 緒論1
1.1 研究動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 文獻回顧. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 研究方向. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 章節介紹. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 實驗方法介紹6
2.1 Delay and Sum 和系統流程介紹. . . . . . . . . . . . . . . . . 7
2.2 Minimum Variance Distortionless Response . . . . . . . . . . 9
2.3 TDOA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3.1 Time Domain Cross-correlation . . . . . . . . . . . . . 13
2.3.2 Frequency Domain General Cross-correlation . . . . . 14
2.3.3 Fractional Delay . . . . . . . . . . . . . . . . . . . . . 16
2.4 Weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3 實驗與結果分析22
3.1 設備與流程. . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 實驗內容與結果. . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2.1 近距離線性排列麥克風錄音. . . . . . . . . . . . . . . 25
3.2.2 遠距離線性排列麥克風錄音. . . . . . . . . . . . . . . 27
3.2.3 遠距分散排列麥克風錄音. . . . . . . . . . . . . . . . 28
3.2.4 情境模擬雜訊. . . . . . . . . . . . . . . . . . . . . . . 29
3.3 結果效益評估. . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4 結論與未來展望37
4.1 結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.2 未來展望. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

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