小波轉換及其理論為近幾年來相當熱門的研究主題之一﹐目前小波轉換已被廣泛地應用在信號處理﹑影像和音訊處理﹑通信系統以及應用數學等不同的研究領域。由於小波轉換具有極佳的時域-頻域分析功能以及多重解析的特性﹐因此非常適合運用在具有高時變性的語音信號上。本論文研究主題為小波轉換在語音信號處理上之應用﹐並針對語音信號提出一套以小波轉換為基礎的基本處理架構。在此架構下﹐本論文發展出音高週期求取﹑子母音分割,語音雜訊消除以及語音信號偵測等四項新演算法。此外﹐針對小波轉換濾波器組架構中所衍生的瑕頻干擾﹐本論文同時提出一套瑕頻補償演算法來加以克服。
首先﹐在音高週期求取部份﹐為加強傳統演算法抗雜訊的能力﹐本論文運用具瑕頻補償的小波轉換以及空間相關函數來改良在雜訊環境中音高週期偵測之準確性﹐實驗結果顯示本論文所提的新演算法無論在乾淨或雜訊環境中均具有最佳的成效。其次﹐在子母音分割方面﹐本論文提出一套以小波轉換搭配積函數的創新演算法﹔與傳統方法比較之下﹐本論文所提的方法不需要進行音高週期求取以及逆搜尋動作﹐同時可提昇子母分割的準確度。另外在語音雜訊消除方面﹐本論文運用人耳聽覺的特性發展出一套接近巴克頻譜的小波分解架構﹐並配合時變性鄰界值演算法進行除雜訊處理﹔本論文所提的方法改良了傳統小波除雜訊系統中常見的語音信號過度抑制的缺點﹐同時可提升除雜訊後語音信號的音質。最後本論文進一步利用在語音雜訊消中所研發的時變性鄰界值演算法於語音信號偵測﹐實驗結果顯示本論文所提的方法在高雜訊環境下仍有極高的語音信號偵測率。

Wavelet transform and its theory is one of the most exciting developments in the last decade. In fact, the wavelet transform has been developed independently for various fields such as signal processing, image processing, audio processing, communication, and applied mathematics. Due to the wavelet representation has characteristics of the efficient time-frequency localization and the multi-resolution analysis, the wavelet transforms are suitable for processing the non-stationary signals such as speech. Therefore, this thesis focuses on the study of wavelet-based speech signal processing and proposes a framework of speech signal processing using wavelet transform. Based on the proposed framework, this thesis develops four new wavelet-based speech signal processing algorithms including pitch detection, consonant/vowel (C/V) segmentation, speech enhancement, and voice active detection (VAD). Furthermore, in order to cancel out the aliasing distortion arose in the filterbank structure of wavelet transforms, this thesis also proposes an aliasing compensation algorithm to overcome this problem.
The first part illustrated in this thesis is the wavelet-based pitch detection algorithm. This thesis applies the aliasing compensated wavelet transform and the modified spatial correlation function to improve the robustness of conventional pitch detection algorithms under noisy environments. Experimental results show the proposed pitch detection algorithm has the better performance than those of conventional algorithms no matter under clear or noisy environments.
The second part of this thesis presents the wavelet-based C/V segmentation algorithm. This novel algorithm can directly detect the C/V segmentation point by the use of the product function and its energy profile. In comparison with conventional C/V segmentation algorithms, the proposed algorithm is no need to use pitch detector as well as backward processing. As a consequence, the accuracy of the proposed C/V segmentation algorithm can be increased substantially from those of conventional approaches.
In the third part, this thesis proposes a wavelet-based speech enhancement method based on the perceptual wavelet packet decomposition (PWPD) and the time-adapted thresholding (TAT) in order to increase the perceptual speech quality after enhancement processing. With these improved techniques, the over thresholding of speech segments which is usually occurred in conventional speech enhancement schemes can be avoided. In addition, the advantage of this improved method is that it does not require a complicated estimation of the noise level or any knowledge of the SNR. Using both additive and real noises, experimental results demonstrate that the speech enhancement method proposed in this thesis is capable of outperforming conventional noise cancellation schemes.
Finally, this thesis further applies the TAT algorithm developed in the third part to the application of VAD. This new wavelet-based VAD method also has the advantage that it needs not a complicated estimation of the noise level or any knowledge of the SNR. Experimental results show this new type of VAD method has an accurate detection rate even through the speech signal is seriously contaminated by the background noise.

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