近年來已有許多聲源辨位之研究論文出現，但綜觀目前聲源辨位系統，大多數的音源方向估測技術會因為訊號反射或噪音問題會造成各頻帶間之時間差不一致，因此本論文提出一種可應用於噪音環境下之音源方向估測技術，本系統包括四個部分：相位差近似單元、訊號矩陣重建單元、頻帶過濾單元及訊號來源偵測演算法，其中相位差近似技術利用多次一階線性回歸估測出理想的相位差曲線。訊號矩陣重建單元重建共變異矩陣，可有效地降低系統估測之誤差。頻帶過濾單元利用子空間演算法的概念，將訊號矩陣經由特徵值分解後求出訊號特徵值及噪聲特徵值比值，用以找出包含語音訊號之頻帶。
在實驗結果證明本論文所提出之系統可有效降低系統估測之平均誤差至15度以下，且對於45度以下及135度以上之測試角度比傳統音源估測演算法可以更有效的減少平均誤差達20度。

In the recent years, Direction of Arrival (DOA) techniques have been intensively studied. However, noise and reverberation effects still cause huge problems to DOA research. In order to alleviate such problems, we propose a pre-processing method that can solve the reverberation and noise problems with only two microphones. There are four parts in our propose system: Linear phase-difference approximation, covariance matrix reconstruction, frequency-bin selection, and DOA algorithm.
First, the linear phase-difference approximation technique and covariance matrix reconstruction can initially solve reverberation and noise influence, and adjust the original covariance matrix to a better one. At the next stage, the frequency-bin selection is used to filter those frequencies, which contain more noise information, and reserves frequency bands for DOA algorithm.
The experimental results prove that the proposed system has good performance with different Signal-to-Noise Ratio (SNR) signal, and the estimation error can be reduced by 20 degrees. This demonstrates the efficacy and feasibility of our proposed methods.

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