語音加強－基於混合式小波臨界值演算法於有色雜訊的刪減

Speech Enhancement Based on Hybrid Wavelet Thresholding Algorithm for Reducing Colored Noise

楊瑞政(Jui-Cheng Yang)

指導教授 ： 陳國泰 ； 郭崇仁

崑山科技大學/工程學院/電機工程研究所/碩士(2006年)

摘要

在小波去雜訊方法中，最主要的是由Donoho和Johnstone所提出的小波縮減演算法，對被干擾的訊號作小波轉換，從轉換來的小波係數求取一個臨界值，再使用硬式或軟式縮減函數作雜訊的刪減，但它侷限於單一個臨界值上，且此方法只實驗在白色雜訊的刪減上。所以針對自然環境中的有色雜訊，本論文提出了一個有效的雜訊刪減方法。首先，對離散小波包裹轉換來的每個包裹小波係數發展出兩個不同的臨界值，再利用這兩個不同的臨界值結合新的混合式縮減函數，來作有色雜訊的刪減。最後我們以此新的去雜訊方法應用在語音加強上，針對行駛中的車內雜訊、風扇雜訊等有色雜訊，結果顯示出此新發展的方法能有效的濾除有色雜訊，達到改善語音品質的目的。

關鍵字

語音加強 ； 小波轉換 ； 小波去雜訊

並列摘要

The famous wavelet denoising method is wavelet shrinkage algorithm proposed by Donoho and Johnstone. It transforms the degraded signal by wavelet to produce the wavelet coefficients, which is utilized to evaluate a threshold value to determine the (hard thresholding or soft thresholding) wavelet shrinkage function. These methods were only experimented on white noise suppression. Thus, we proposed an effective method to reducing colored noise. First, we developed two different thresholds from wavelet-packet coefficients produced by discrete wavelet-packet transform. Furthermore, we applied these thresholds to the new hybrid wavelet shrinkage function to suppress the colored noise. Finally, we applied this new wavelet denoising algorithm to enhance speech corrupted by colored noise such as car noise and fan noise. In these applications, signal-to-noise ratio (SNR) has been used to evaluate the performances, which show that this new wavelet denoising algorithm can suppress colored noise effectively to improve the speech quality.

並列關鍵字

speech enhancement ； wavelet transform ； wavelet denoising

參考文獻

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