在本論文中，以聲源定位和分離的觀點提出噪音消除的演算法，最小能量無失真響應演算法(Minimum Power Distortionless Response, MPDR)被使用於決定訊號與噪音源之軸承，提可諾夫正規化(Tikhonov regularization ,TIKR)及壓縮感知(compressive sensing ,CS)演算法被用以擷取訊號與噪音源之振幅。為了評估所提出的方法，採用對數最小均方誤差(log minimum mean-square error, log-MMSE)演算法、廣義旁瓣消除器(Generalized Sidelobe Canceller, GSC)及正規多輸入輸出反運算理論(regulated multiple-input/output inverse theorem, R-MINT)作為基準的方法。對數最小均方誤差使用後置濾波器去估計優化增益校正函數；而為了強化廣義旁瓣消除器，合併了次帶濾波及內部迭代法，即稱之為GSC-SB-IIT演算法；R-MINT被應用於房間響應反運算濾波。在數值模擬和實驗上，使用24個通道的均勻圓形麥克風陣列來執行，且用白噪音及交通噪音來模擬背景噪音。客觀量測的分段訊噪比及語音品質感知評估和主觀的聆聽測試被用來比較噪音消除的方法，結果顯示，感知壓縮演算法達到了最好的噪音消除性能。

In this thesis, a noise reduction algorithms is presented from the perspective of source localization and separation. Minimum Power Distortionless Response (MPDR) algorithm is utilized to determine the bearings of the signal and noise sources. Tikhonov regularization (TIKR) and compressive sensing (CS) algorithm are employed to extract the amplitudes of the signal and noise sources. In order to evaluate the proposed method, the log minimum mean-square error (log-MMSE) algorithm, the Generalized Sidelobe Canceller (GSC), and the regulated multiple-input/output inverse theorem (R-MINT) are adopted as benchmarking methods. The Log-MMSE is used to estimate an optimize gain correction function as a post-filter. To enhance GSC, subband (SB) filtering and internal iteration (IIT) are incorporated, which is termed the GSC-SB-IIT method. The R-MINT used to be applied in room response inverse filtering. Numerical simulations and experiments are conducted for a 24-channel uniform circular microphone array. White noise and traffic noise are used in simulating the background noise. Objective tests based on the segmental signal-to-noise ratio (segSNR) and Perceptual Evaluation of Speech Quality (PESQ) and subjective listening tests are conducted to compare the noise reduction approaches. The results show that the CS algorithm has achieved the highest reduction of noise.

TABLE OF CONTENTS

摘　　要 i
ABSTRACT ii
誌 謝 iii
TABLE OF CONTENTS iv
LIST OF TABLES v
LIST OF FIGURES vi
Chapter 1 INTRODUCTION 1
Chapter 2 THREE CONVENTIONAL NOISE REDUCTION METHODS 5
2.1 Single Channel Noise Reduction Based on Log-MMSE Algorithm 5
2.2 Robust Generalized Sidelobe Canceller 6
2.3 Regularized Multiple-Input/Output Inverse Theorem (R-MINT) Method 9
Chapter 3 NOISE REDUCTION BASED ON INVERSE RECONSTRUCTION
15
3.1 Source Localization 15
3.2 Source Signal Separation 16
3.2.1 Tikhonov regularization (TIKR) method 16
3.2.2 Compressive sensing (CS) method 17
Chapter 4 NUMERICAL AND EXPERIMENTAL INVESTIGATIONS 19
Chapter 5 CONCLUSIONS 39
REFERENCES 40

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