本論文提出了基於頻譜刪減法(spectral subtraction)的二階段語音增強以消除背景雜訊。頻譜刪減法的概念是直接在頻域中將雜訊成份刪減掉，因此，預估出雜訊的功率頻譜之後，可利用頻譜刪減法得到刪減過後的訊號。然而，由於頻譜刪減法存在著缺陷，使得刪減過後的訊號仍然存在一定程度的剩餘雜訊(residual noise)。為了解決這個問題，我們利用一個頻譜權重濾波器(spectral weighted filter)將頻譜刪減法刪減過後的訊號作進一步雜訊濾除。濾波器是根據輸入的刪減後訊號做調整，當我們可以確保輸入濾波器的刪減後訊號只有剩餘雜訊時，藉由將權重濾波器的值設為零，可抑制仍有的剩餘雜訊；令一方面，考量人類聽覺中的遮蔽效應，我們假設輸入濾波器的刪減後訊號中，同時含有語音訊號與雜訊訊號，而語音訊號的強度可遮蔽雜訊訊號，使得人耳察覺不出噪音的存在。如此，我們便可忽視此噪音的存在，藉由設定濾波器的值為一，以保留語音訊號的資訊。在模擬結果中，我們強調加入權重濾波器前後方法的比較，並利用訊噪比改善(SNR improvement)與提昇率(improvement rate)等客觀評估方法。從實驗數據中，我們發現加入權重濾波器的確可以提昇頻譜刪減法的效能。

This thesis presents a two-step spectral subtraction speech enhancement algorithm to reduce background noise. First, spectral subtractive-type algorithms are utilized to obtain the subtracted signal by subtracting the noise power spectrum from the noisy power spectrum of noisy speech signals. Due to the inherent deficiency of conventional subtractive-type algorithms, there still remains residual noise in the subtracted signal. To solve this problem, we integrate a spectral weighted filter with subtractive-type algorithm to further eliminate the residual noise. The adjustment of the spectral weighted filter is based on the current spectrum of the subtracted signal. If the current spectrum of subtracted signal is the residual noise, we set the value of the spectral weighted filter close to zero to suppress the residual noise. On the other hand, the value of spectral weighted filter can be set one to keep the information of speech signal by considering the masking effect. The effectiveness of the proposed subtractive-type algorithms coupled with additive spectral weighted filters has been validated by SNR improvement tests and improvement rate tests and compared with conventional subtractive-type algorithms. According to our simulation results, the proposed algorithms outperform the conventional methods in both tests.

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