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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳永耀 | |
dc.contributor.author | Chun-Kai Chang | en |
dc.contributor.author | 張鈞凱 | zh_TW |
dc.date.accessioned | 2021-05-20T20:13:26Z | - |
dc.date.available | 2010-07-27 | |
dc.date.available | 2021-05-20T20:13:26Z | - |
dc.date.copyright | 2009-07-27 | |
dc.date.issued | 2009 | |
dc.date.submitted | 2009-07-23 | |
dc.identifier.citation | [1] K. Khaldi and A. O. Boudraa, 'Speech denoising by Adaptive Weighted Average filtering in the EMD framework,' IEEE Int. Conf. Signals, Circuits and Systems, Nov, 2008.
[2] J.S. Lee. ,'Digital image enhancement and noise filtering by using local statistics,' IEEE Trans. Pattern Anal. Mach. Int., vol.2, issue 4, pp. 165-168, Mar.1980. [3] A.O. Boudraa and J.C. Cexus., 'Denoising via empirical mode decomposition,' In Proc. IEEE ISCCSP, Marrakech, Morocco, 2006. [4] A.O. Boudraa, J.C. Cexus, and Z. Saidi. , “EMD-based signal noise reduction,' Int. J. Sig. Process., vol.1, issue 1, pp. 33-37, 2004. [5] N. E. Huang, Z. Shen and S. R. Long., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,”, Proceedings of the Royal Society of London A(454), pp. 903-995, 1998. [6] Y. Kopsinis, S. McLaughlin, “Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding,” IEEE Trans. Signal Processing, vol.57, NO.4, April.1994. [7] P. Flandrin, G. Rilling, and P. Goncalves, “Empirical mode decomposition as a filter bank, “IEEE Signal Process. Lett., vol. 11, pp. 112–114,Feb. 2004. [8] B. Widrow et al., 'Adaptive Noise Canceling: Principles and Applications,' Proceedings of the IEEE, vol. 63, pp. 1692-1716, Dec. 1975. [9] G. Rilling and P. Flandrin, “One or two frequencies? The empirical mode decomposition answers,” IEEE Trans. Signal Process., pp. 85–95, Jan. 2008. [10] L. Cohen, “Time-frequency distributions-a review,' Proceedings of the IEEE, vol.77, issue 7, pp.941-981, July 1989. [11] N. E. Huang and S.P. Shen, 'Hilbert-Huang transform and its applications,' World Scientific, 2005. [12] Z. Wu and N. E. Huang, 'A Study of the characteristics of white noise using the empirical mode decomposition method,' Proc. Roy. Soc. London A, vol. 460, pp. 1597–1611, Jun. 2004. [13] 葉向林, “聽障者之語音增強與轉換,”國立清華大學電機工程研究所碩士論文, 2004. [14] 陳厚君, “經驗模態分解法之語音辨識,”國立中央大學電機工程研究所碩士論文, 2004. [15] 劉致廷, “座艙語音記錄器之單通道適應性噪音濾除,”國立台灣大學電機工程研究所碩士論文, 2007. [16] 賴亦桓, “運用經驗模態分解法於語者辨識,”國立台灣大學電機工程研究所碩士論文, 2006. [17] NOISEX-92:http://www.speech.cs.cmu.edu/comp.speech/Section1/Data/noisex.html [18] Matlab central: http://www.mathworks.com/matlabcentral/. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/9219 | - |
dc.description.abstract | 說話內容常會因背景聲音太大而聽不清楚 ,如何去將語音訊號中所含的噪音清除或抑制,就是所謂的語音增強技術。傳統在單通道語音增強技術中最常採用溫尼濾波器(Wiener filtering)或是頻譜相減法等方法,但大部分均是在頻域上做處理,經過時頻上的轉換,常有語音失真的情形。
黃鍔博士在1998年提出了一種新的訊號分析法希爾伯特-黃轉換(Hilbert Huang Transform, HHT),其方法是將訊號經由經驗模態分解法(Empirical Mode Decomposition, EMD),將資料變化的內部時間尺度作為特徵而分解成多個內建模態函數(Intrinsic Mode Functions, IMF)分量,這些分量經由希爾伯特轉換(Hilbert Huang Transform) 可得到有物理意義的瞬時頻率。近年來經驗模態分解法被應用在語音增強上,針對白噪音分解後的特性,可對各個IMF分量中所含的噪音量做估測並消除。 在本論文我們針對基於經驗模態分解法的語音增強方法作研究。藉由在含噪訊號中加入人工訊號,噪音主要成分在分解過程中將集中在部份分量,移除這些分量以去除大部分噪音,在配合適應性中間值權重濾波器(Adaptive Center Weighted Average filter, ACWA filter)將語音中殘存的噪音消除。實驗顯示,此方法在低訊噪比下有很好的消噪效果,並且可以保留原先的語音特性。 | zh_TW |
dc.description.abstract | Degradation of the quality of speech caused by the background noise is common in most real situations. How to suppress and remove the noise content in a noisy speech is speech enhancement technique. In traditional signal-channel speech enhancement methods, Wiener filter and spectral subtraction are general methods. But these methods process in frequency domain, the distortion of signal often happen.
A new signal analyzing method, Hilbert-Huang Transform (HHT), was proposed by Norden E. Huang et al. in 1998. With EMD, signal can be decomposed into a finite number of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. These IMFs with Hilbert transform obtain meaningful instantaneous frequencies. In recent years, EMD was used on speech enhancement. After EMD of white noise, noise component of each IMF can be estimated then remove it. In this thesis, we research on speech enhancement with EMD. After adding an artificial signal to noisy signal, most noise component can concentrate on some IMFs. We can remove most noise by throwing away the IMFs. Adaptive center weighted average filter (ACWA filter) is used to whiten the residual noise in speech. These results of experiment show that the method has good performance of de-noising in low SNR situation and reserve the quality of original speech. | en |
dc.description.provenance | Made available in DSpace on 2021-05-20T20:13:26Z (GMT). No. of bitstreams: 1 ntu-98-J96921010-1.pdf: 1456384 bytes, checksum: bf341ef0b42e4048f3fa7fb036225ebe (MD5) Previous issue date: 2009 | en |
dc.description.tableofcontents | 中文摘要 i
Abstract ii Contents iii List of Figures v List of Tables viii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Background 1 1.3 Problem Foundation 2 1.4 Thesis Organization 2 Chapter 2 Preliminaries 4 2.1 Empirical Mode Decomposition 4 2.1.1 Instantaneous Frequency 6 2.1.2 Intrinsic Mode Function 11 2.1.3 Empirical Mode Decomposition Method 13 2.1.4 Hilbert Huang Spectrum 20 2.1.5 Application of EMD 21 2.2 Study of Speech Enhancement Methods 23 2.2.1 Spectral Subtraction 23 2.2.2 Wiener Filtering 24 2.2.3 Adaptive Noise Canceling 25 2.3 Speech Enhancement Methods by Empirical Mode Decomposition 28 2.3.1 Speech Enhancement Method with Filtering 29 2.3.2 Speech Enhancement Method with Thresholding 32 Chapter 3 Speech Enhancement with Additive Signal Base on Empirical Mode Decomposition 34 3.1 Problem Foundation 34 3.2 Analysis of Signal with Empirical Mode Decomposition 35 3.2.1 White Noise 35 3.2.2 Sinusoidal Signal 38 3.2.3 Summary 41 3.3 Proposed Speech Enhancement Method with Empirical Mode Decomposition 41 3.3.1 Architecture 41 3.3.2 Dynamic Filter 42 3.3.3 Adaptive Center Weighted Average Filter 48 Chapter 4 Experiment Results 51 4.1 Test Environment and Noisy Speech Database 51 4.2 Speech Quality Assessment 52 4.3 Experimental Results 53 Chapter 5 Conclusions 63 References 64 | |
dc.language.iso | en | |
dc.title | 加入人工訊號於經驗模態分解法中在語音增強上的研究 | zh_TW |
dc.title | Study of Empirical Mode Decomposition in Speech Enhancement with Artificial Additive Signal | en |
dc.type | Thesis | |
dc.date.schoolyear | 97-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 連豊力,嚴家鈺 | |
dc.subject.keyword | 經驗模態分解法,消噪,希爾伯特轉換, | zh_TW |
dc.subject.keyword | Empirical Mode Decomposition,De-noising,HHT, | en |
dc.relation.page | 65 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2009-07-23 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
顯示於系所單位: | 電機工程學系 |
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