臺灣博碩士論文加值系統

對於訊號分析方面，經驗模態分解法是一個很好用的方法，它具有自適應性(Adaptive)，並能夠處理非線性(Nonlinear)與非穩定(Nonstationary)的訊號，且能在時域上直接訊號拆解，但它會有模態混雜(Mode mixing)的問題。為了解決此問題，Wu與Huang利用白雜訊(White noise)發展了總體經驗模態分解法(Ensemble empirical mode decomposition, EEMD)，但此方法最後的結果是透過多次迭代而產生的，因此運算時間會增加很多。
本文針對語音訊號，我們使用Shaped noise取代白雜訊來加速EEMD收斂的速度，以此降低EEMD迭代的次數。Shaped noise或稱為訊號頻譜相依雜訊(Signal-spectrumdependent noise, SSDN)，它可以隨著輸入訊號的頻譜特性而隨機改變，且具有自適應性的特性，相較於對訊號加入白雜訊，它可以節省對無能量的頻率的運算，大大的省掉多餘的運算，並且降低運算的時間。
本文的實驗中，我們也發現，針對語音訊號，我們可以利用特性較接近語音訊號的雜訊來取白雜訊時，例如粉紅雜訊(Pink noise)與紅雜訊(Brown noise)，也能降低EEMD迭代的次數，降低運算的時間。

In Signal Processing, Empirical mode decomposition (EMD) is one of the useful approaches for processing nonlinear and nonstationary signals. It is adaptive to input signal and could decompose signal straightly in time domain. However, its shortcomings include mode mixing and end effects that usually appear in the decomposed bands. Although a noise-assisted data analysis (NADA) called ensemble empirical mode decomposition (EEMD) has been proposed to circumvent this problem, doing so also results in an inevitably long computation for alleviating the mode mixing. In this paper, we use shaped noise instead of white noise as a disturbance for a fast convergence of EEMD. The signal-spectrum-dependent noise (SSDN) is able to effectively randomize the targeted signal in time domain, and then significantly save the superfluous calculation around the corresponding energy-free frequencies. The experimental results also show that both pink noise and brown noise outperform the white noise in terms of computation for the EEMD of speech-like signal.

摘要 II
Abstract III
致謝 IV
目錄 V
圖片索引 VIII
表索引 XI

第一章 導論 1
1.1 前言 1
1.2 時頻分析回顧 2
1.3 研究動機 4
1.4 本文架構 5

第二章 時頻分析與雜訊輔助資料分析 6
2.1 時頻分析基本理論 6
2.1.1 短時傅立葉轉換 7
2.1.2 離散小波轉換 8
2.1.3 希爾伯特-黃轉換 9
2.1.4 時頻分析法綜合比較 11
2.2 雜訊輔助資料分析系統 12
2.2.1 雜訊輔助分析理論簡介 13
2.2.2 相關研究 13

第三章 總體經驗模態分解法 16
3.1 經驗模態分解法 16
3.1.1 本質模態函數 16
3.1.2 篩選程序 17
3.2 EMD特性與存在的問題 21
3.2.1 EMD基本特性 21
3.2.2 模態混雜問題 22
3.2.3 混波問題 24
3.3 總體經驗模態分解法 28
3.4 二維經驗模態分解法 31
3.4.1 影像融合 32

第四章 Shaped Noise 38
4.1 Colored noises 38
4.1.1 白雜訊 38
4.1.2 粉紅雜訊 40
4.1.3 紅色雜訊 43
4.2 Signal-spectrum-dependent noises (SSDN) 45

第五章 實驗量測與結果 50
5.1 實驗方法 50
5.2 訊號誤差比 52
5.3 實驗結果 53
5.3.1 單音節實驗 53
5.3.2 多音節實驗 59
5.4 實驗結果綜合分析 64

第六章 結論與未來展望 66
6.1 結論 66
6.2 未來展望 66

參考文獻 68

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