本論文提出應用遠場麥克風陣列技術於噪音消除系統的方法，來抑制周遭環境雜訊，其目的在於消減主要語音訊號以外的噪音，使信噪比得以提升，另外，對於突然開啟或關閉的階躍噪音提出有效的解決方法。前端處理部分，將訊號用傅立業轉換至頻率域後，利用聲源在空間聲場中方向獨立的特性，使用最小能量無失真響應法(MPDR)波束形成，設計麥克風陣列的指向性，加強主要語音聲源方向過來的訊號，同時抑制來自其他方向的訊號干擾，以達到在空間上濾波的效果。經過波束形成處理後，可得到一個單通道的語音訊號，後端部分再藉由對數最小均方誤差估計器，估計出一個最佳化修正增益函數，將訊號再乘上此增益函數去雜訊處理後，再利用反傅立業轉換及重疊相加法轉回至時域，最後將可得到乾淨的主要語音訊號。此演算法架構能有效地消除主軸以外的干擾源，並消除主軸內的訊號雜訊。為了評估訊號處理後之結果與原始訊號之差異，透過主觀聆聽測試、客觀的語音品質感知試驗、分段信噪比試驗及語者辨識實驗皆可驗證此演算法消除噪音的能力。

This thesis proposes a noise reduction system that takes advantage of far-field microphone array beamforming technology and combines with statistical-model based noise reduction algorithm in order to suppress noise and improve the signal-to-noise (SNR) ratio. This thesis also provides an effective solution for such situations that noises turn on and off as a step signal in real time implementation. For the front-end processing, the signal is converted to frequency domain by Fourier transform. This thesis adopts the property that the direction of each source is independent. Applying Minimum Power Distortionless Response (MPDR) beamformer, highly directional microphone array is designed to focus on the direction of main speech signals and suppress noise and interference from the background. After the beamforming processing, a single-channel signal is produced. Using Log Minimum Mean Square Error (Log-MMSE) estimator to estimate an optimize gain correction function as a post-filter, clean main speech signal can be obtained. This algorithm architecture can effectively reduce the interference on side-lobe and stationary noise on main-lobe. For evaluating the difference between enhanced signal and original signal, subjective listening test and objective Perceptual Evaluation of Speech Quality (PESQ), Segmental SNR (segSNR) test are applied. Automatic Speech Recognition (ASR) experiment shows the high performance in NR.

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