Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/7825
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dc.contributor溫志煜zh_TW
dc.contributorChih-Yu Wenen_US
dc.contributor廖珗洲zh_TW
dc.contributor.advisor吳國光zh_TW
dc.contributor.advisorKuo-Guan Wuen_US
dc.contributor.author侯政宇zh_TW
dc.contributor.authorHou, Cheng-Yuen_US
dc.contributor.other中興大學zh_TW
dc.date2008zh_TW
dc.date.accessioned2014-06-06T06:40:36Z-
dc.date.available2014-06-06T06:40:36Z-
dc.identifierU0005-3008200714233100zh_TW
dc.identifier.citation[1] Thomas F. Quatieri, Discrete-Time Speech Signal Processing: Principles and Practice. Pearson education Taiwan Ltd, Prentice Hall PTR, Vol. 13, pp. 665-708, Fed. 2005. [2] Y. Ephraim and D. Malah, “Speech Enhancement Using a Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator,” IEEE Trans. Acoustics, Speech, Signal Processing, Vol. 32 (6), pp. 1109-1211, Dec. 1984. [3] Y. Ephraim and D. Malah, “Speech Enhancement Using a Minimum Mean-Square Error Log-Spectral Amplitude Estimator,” IEEE Trans. Acoustics, Speech, Signal Processing, Vol. 33 (2), pp. 443-445, Dec. 1985. [4] C.H. You, S.N. Koh, and S. Rahardja, “β-order MMSE spectral amplitude estimation for speech enhancement,” IEEE Trans. Speech Audio Processing. Vol. 13 (4), pp. 475-486, July 2005. [5] C.H. You, S.N. Koh, and S. Rahardja, “Adaptiveβ-Order MMSE Estimation for Speech Enhancement,” Proc. IEEE Int. Conf. Acoust., Speech and Signal Processing, ICASSP-3, Vol. 1, pp. 852-855, 2003. [6] C.H. You, S.N. Koh, and S. Rahardja, “Masking-basedβ-order MMSE speech enhancement,” Speech Communication 48, pp.57-80, 2006. [7] Wu, W. R. and P. C. Chen, “Adaptive AR Modeling in White Gaussian Noise, ” IEEE Trans. Signal Processing, Vol. 45, pp. 1184-1192, May 1997 [8] John R. Treichler, C. Richard Johnson, Jr.,and Michael G. Larimore, Theory and Design of Adaptive Filter. Tom Robbins, Prentice Hall, Inc., Vol. 4, pp. 92-115, 2001. [9] J. S. Lim, “Evaluation of a correlation subtraction method enhancing speech degraded by additive white noise,” IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-26, no. 5, pp. 471-472, Oct. 1978. [10] R. Martin, “Spectral subtraction based on minimum statistics,” in Proc. Eur. Signal Processing Conf., 1994, pp. 1182-1185.zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/7825-
dc.description.abstract語音增強技術的發展,是為了消除干擾雜訊的影響,來改善音質、增加語音清晰度,或減少聽者的疲勞。常見的語音增強方法有頻譜刪減法(Spectral Subtraction),最小均方誤差法(Minimum Mean-Square Error, MMSE)。此二類方法,都必須先取得訊號的頻譜值,(頻譜消去法中需要知道雜訊頻譜值,而最小均方誤差法中需要已知語音頻譜值),才能進行消除雜訊的處理。然而,當輸入訊號只有包含雜訊干擾的語音時,如何取得上述之參數,便成為一個需要解決的問題。 傳統上,取得雜訊頻譜的方法是藉由所謂的VAD (Voice Activity Detector),根據個別處理的音框的特性,判斷其為雜訊音框或是含語音之音框,進而產生新的雜訊音框值。另一方面,傳統上取得語音頻譜的方法是以前一個音框在消除完雜訊後的輸出訊號頻譜,當作目前音框中的語音訊號頻譜值。 在本篇論文中,我們提出根據語音訊號之AR模型分析的語音頻譜及雜訊頻譜值估計方法,可以得到比傳統方法更準確的估計結果,並提升雜訊消除後的效能。zh_TW
dc.description.abstractThe objective of developing speech enhancement (SE) technology for eliminating noise disturbance is to improve the overall quality, to increase intelligibility, or to reduce listener fatigue by suppressing noise and eliminating the distortion. There are two methods for speech enhancements, one is spectral subtraction (SS), and the other is minimum mean-square error (MMSE). First, we have to calculate the signal spectrum for denosing process in these two methods. The former requires the noise spectrum, and the latter requires the speech spectrum. Nevertheless, how to calculate the above parameters become a problem we have to solve only given a noisy speech signal. The conventional approach to noise spectrum estimation is called “VAD” (Voice Activity Detector), which can detect where the processing frame is a speech presence one or not, and estimate a new noise based on the characteristics of the processing frame. On the other hand, the conventional method for obtain the speech spectrum is to utilize the denoised signal spectrum of last frame instead of current speech spectrum. In this thesis, we propose an approach to the speech spectrum and noise spectrum estimation based on the analysis of AR model of the speech signal. In contrast, our proposed approach outperforms the conventional methods on estimating and denoising.en_US
dc.description.tableofcontents摘要 ……………………………………………………………………………… i Abstract ………………………………………………………………………… ii 目次……………………………………………………………………………… iii 圖表目次…………………………………………………………………………. v 第一章 序論………………………………………………………………… 1 1.1 研究背景…………………………………………………….............. 1 1.2 研究動機……………………………………………………………… 1 1.3 章節概要…………………………………………………….............. 2 第二章 語音增強的方法…………………………………………………... 3 2.1 傳統的語音增強法…………………………………………............. 3 2.1.1 Wiener濾波器………………………………………………… 3 2.1.2 頻譜刪減法(Spectral Subtraction)…………………………... 4 2.2 最佳估計方法………………………………………………………... 5 2.2.1 最小均方誤差(Minimum Mean-Square Error)……………… 5 2.2.2 適應性β最小均方誤差法估計法…………………………… 6 2.2.2.1 β-MMSE STSA Estiamtion……………………………. 6 2.2.2.2 Adaptive β……………………………………………… 7 2.3適應性演算法………………………………………………................. 9 2.3.1LMS(Least Mean Square)…………………………….…………. 9 2.3.2NLMS(normalized Least Mean Square)…………….……….... 10 2.3.3ρ-LMS演算法………………………………….………….. 10 2.3.4ρ-LMS之改進………………………………….………….. 12 第三章 新的語音增強演算法………………………………….………… 14 3.1 即時訊號頻譜估計……………………………………….………… 14 3.2 雜訊音框判別與即時雜訊頻譜估計……………………………… 15 第四章 實驗結果………………………………………………………….. 17 4.1 訊號頻譜估計結果…………………………………………………. 17 4.2 雜訊頻譜估計結果…………………………………………………. 18 4.3 消除雜訊的效果……………………………………………………. 19 第五章 結論………………………………………………………………….. 34 參考文獻……………………………………………………………………….. 35zh_TW
dc.language.isoen_USzh_TW
dc.publisher電機工程學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-3008200714233100en_US
dc.subjectSpeech Enhancementen_US
dc.subject語音增強zh_TW
dc.subjectNoise Cancellationen_US
dc.subject噪音消除zh_TW
dc.title使用ρ-LMS於即時頻譜估計之語音增強研究zh_TW
dc.titleOn-line Spectrum Estimation for Speech Enhancement Using ρ-LMSen_US
dc.typeThesis and Dissertationzh_TW
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en_US-
item.openairetypeThesis and Dissertation-
item.grantfulltextnone-
item.fulltextno fulltext-
item.cerifentitytypePublications-
Appears in Collections:電機工程學系所
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