本論文的研究重點在於提出了一個基於噪音偵測、消除以及透過線性平滑濾波器抑制殘餘噪音的語音增強演算法。
在本論文中，我們特別強調要處理的是與語音訊號頻率分佈相近的同頻域噪音消除演算法。本演算法源於主動式噪音控制和語音增強濾波器的觀念，但於噪音插入點偵測以及頻譜刪減演算法當中具有其獨創性。在論文當中首先說明如何克服訊號漂移的困難，利用統計噪音訊號穩定度的觀念先建構出一個穩定的噪音樣本。接著發展出一個透過此穩定的噪音樣本與被噪音污染的訊號源能量分析而能夠偵測噪音源插入點的『Diff-RMS』演算法，以及在搜尋到插入點之後透過離散餘弦轉換將噪音與被污染訊號皆轉換至頻域上做噪音消除的動作。而此一消噪後的訊號則經由離散餘弦逆轉換將其轉換回時域當中。最後則透過一個線性的平滑濾波器將聲音訊號中的殘餘噪音能量抑制到人耳不可聽聞之程度。全部過程當中同時具有語音訊號在時域和頻域的處理觀念，以及許多聲學上的數位訊號處理技巧。這些也是我們論文中會一一探討的部分。
最後並將本噪音消除演算法實現於嵌入式系統發展平台上，驗證其能夠穩定的運行，並且進一步符合未來實用的價值。

The practical thrust of this thesis is to propose a new speech enhancement algorithm based on noise detection/cancellation and depress the remnant noise by a linear smooth filter.
In this thesis, we put special emphasis on that the frequency distribution of noise we want to cancel is the same as the frequency distribution of speech signal in our algorithm. The new algorithm based on the concept of active noise control and speech enhancement filter, but has the originality in noise corrupted point detection and spectral-subtraction process. First of all, we explain how to win through the difficulty of signal drift by using the method of construct a stable noise template with statistics. Second, we develop a noise detection algorithm named "Diff-RMS algorithm" which analyze the energy of noise and noise corrupted signal. After we find the correct noise corrupted point, we adaptively subtract the coefficients of discrete cosine transform of noise template form an input signal. When subtraction is done, we convert the entire processed coefficients form frequency domain back to time domain by inverse discrete cosine transform. In the end, we apply a linear smooth filter to depress the remnant noise to an inaudible degree.All of the thetechnological processes contains acoustics concepts of digital signal processing in both time domain and frequency domain and that will be discussed in the following chapter in the thesis.
Finally, we implement the new noise cancellation algorithm on embedded platform to verify the stability and future practicability.

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