跳到主要內容

臺灣博碩士論文加值系統

(35.173.42.124) 您好!臺灣時間:2021/07/26 14:18
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:游明展
研究生(外文):Ming-jhan You
論文名稱:利用頻譜權重濾波器改善頻譜刪減法於單一通道語音增強
論文名稱(外文):An Improved Spectral Subtractive-Type Algorithm with a Spectral Weighted Filter for Single-Channel Speech Enhancement
指導教授:王振興王振興引用關係
指導教授(外文):Jeen-shing Wang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:57
中文關鍵詞:頻譜刪減語音增強
外文關鍵詞:spectral subtractionspeech enhancement
相關次數:
  • 被引用被引用:1
  • 點閱點閱:136
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本論文提出了基於頻譜刪減法(spectral subtraction)的二階段語音增強以消除背景雜訊。頻譜刪減法的概念是直接在頻域中將雜訊成份刪減掉,因此,預估出雜訊的功率頻譜之後,可利用頻譜刪減法得到刪減過後的訊號。然而,由於頻譜刪減法存在著缺陷,使得刪減過後的訊號仍然存在一定程度的剩餘雜訊(residual noise)。為了解決這個問題,我們利用一個頻譜權重濾波器(spectral weighted filter)將頻譜刪減法刪減過後的訊號作進一步雜訊濾除。濾波器是根據輸入的刪減後訊號做調整,當我們可以確保輸入濾波器的刪減後訊號只有剩餘雜訊時,藉由將權重濾波器的值設為零,可抑制仍有的剩餘雜訊;令一方面,考量人類聽覺中的遮蔽效應,我們假設輸入濾波器的刪減後訊號中,同時含有語音訊號與雜訊訊號,而語音訊號的強度可遮蔽雜訊訊號,使得人耳察覺不出噪音的存在。如此,我們便可忽視此噪音的存在,藉由設定濾波器的值為一,以保留語音訊號的資訊。在模擬結果中,我們強調加入權重濾波器前後方法的比較,並利用訊噪比改善(SNR improvement)與提昇率(improvement rate)等客觀評估方法。從實驗數據中,我們發現加入權重濾波器的確可以提昇頻譜刪減法的效能。
This thesis presents a two-step spectral subtraction speech enhancement algorithm to reduce background noise. First, spectral subtractive-type algorithms are utilized to obtain the subtracted signal by subtracting the noise power spectrum from the noisy power spectrum of noisy speech signals. Due to the inherent deficiency of conventional subtractive-type algorithms, there still remains residual noise in the subtracted signal. To solve this problem, we integrate a spectral weighted filter with subtractive-type algorithm to further eliminate the residual noise. The adjustment of the spectral weighted filter is based on the current spectrum of the subtracted signal. If the current spectrum of subtracted signal is the residual noise, we set the value of the spectral weighted filter close to zero to suppress the residual noise. On the other hand, the value of spectral weighted filter can be set one to keep the information of speech signal by considering the masking effect. The effectiveness of the proposed subtractive-type algorithms coupled with additive spectral weighted filters has been validated by SNR improvement tests and improvement rate tests and compared with conventional subtractive-type algorithms. According to our simulation results, the proposed algorithms outperform the conventional methods in both tests.
CHINESE ABSTRACT i
ABSTRACT ii
ACKNOWLEDGEMENT iii
LIST OF TABLES vi
LIST OF FIGURES vii
1 Introduction 1-1
1.1 Motivation 1-1
1.2 Literature Survey 1-2
1.3 Purpose of the Study 1-4
1.4 Organization of the Thesis 1-5
2 Noise Estimation 2-1
2.1 Introduction 2-1
2.2 Short-time analysis 2-2
2.3 Time-Varying Recursive Averaging 2-4
2.4 Signal Absence Probability 2-6
3 Subtraction Rule 3-1
3.1 Introduction 3-1
3.2 Subtractive-Type Algorithms 3-2
3.2.1 Power Spectral Subtraction Algorithm 3-2
3.2.2 Spectral Over-Subtraction Algorithm 3-4
3.2.3 Multi-Band Spectral Subtraction Algorithm 3-8
3.3 Spectral Weighted Filter 3-10
3.3.1 Optimization in Mean Square Error 3-12
3.3.2 Masking Threshold 3-15
4 Simulation Results 4-1
4.1 Speech Data 4-1
4.2 Noise Data 4-2
4.3 Data for simulation 4-3
4.4 Performance Evaluation 4-6
4.4.1 SNR Improvement 4-6
4.4.2 Improvement Rate 4-13
4.4.3 Time Waveforms and Spectrograms 4-17
5 Conclusions and Future Work 5-1
5.1 Conclusions 5-1
5.2 Future Work 5-2
References
[1] F. Jabloun and B. Champagne, “A multi-microphone signal subspace approach for speech enhancement,” in Proc. 2001 IEEE Int. Conf. Acoustics, Speech, Signal Processing, vol. 1, pp. 205-208, May 2001.
[2] J. S. Lim and A. V. Oppenheim, “Enhancement and bandwidth compression of noisy speech,” in Proc. IEEE, vol. 67, no. 12, pp. 1586-1604, Dec. 1979.
[3] P. Scalart and J. V. Filho, “Speech enhancement based on a priori signal to noise estimation,” in Proc. 21st IEEE Int. Conf. Acoustics, Speech, Signal Processing, vol. 2, pp. 629-632, May 1996.
[4] R. Martin, “Speech enhancement using MMSE short time spectral estimation with gamma distributed speech priors,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, vol. 1, pp. 253-256, May 2002.
[5] 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, no. 6, pp. 1109-1121, Dec. 1984.
[6] 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, no. 2, pp. 443-445, Apr. 1985.
[7] I. Cohen, “Speech enhancement using a noncausal a priori SNR estimator,” IEEE Signal Processing Letters, vol. 11, no. 9, pp. 725-728, Sep. 2004.
[8] S. F. Boll, “Suppression of noise in speech using the SABER method,” in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, vol. 3, pp. 606-609, Apr. 1978.
[9] S. F. Boll, “Suppression of acoustic noise in speech using spectral subtraction,” IEEE Trans. Acoustics, Speech, and Signal Processing, vol. ASSP-29, pp. 113-120, Apr. 1979.
[10] M. Berouti, R. Schwartz, and J. Makhoul, “Enhancement of speech corrupted by acoustic noise,” in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, pp. 208-211, Apr. 1979.
[11] S. D. Kamath and P. C. Loizou. “A multi-band spectral subtraction method for enhancing speech corrupted by colored noise,” in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, vol. 4, pp. 4160-4164, 2002.
[12] B. L. Sim, Y. C. Tong, J. S. Chang, and C. T. Tan, “A parametric formulation of the generalized spectral subtraction method,” IEEE Trans. Speech and Audio Processing, vol. 6, no. 4, pp. 328-337, Jul. 1998.
[13] N. Virag, “Single channel speech enhancement based on masking properties of the human auditory system,” IEEE Trans. Speech and Audio Processing, vol. 7, no. 2, pp. 126-137, Mar 1999.
[14] J. Poruba, “Speech enhancement based on nonlinear spectral subtraction,” in Proc. of 2002 IEEE 4th Int. Caracas Conf. on Devices, Circuits and Systems, pp. T031-1- T031-4, Apr. 2002.
[15] I. Cohen, “Noise spectrum estimation in adverse environment: improved minima controlled recursive averaging,” IEEE Trans. Speech and Audio Processing, vol. 11, no. 5, pp.466-475, 2003.
[16] J. F. Lynch, J. G. Josenhans, and R. E. Crochiere, “Speech/silence segmentation for real-time coding via rule based adaptive endpoint detection,” in Proc. IEEE ICASSP, vol. 12, pp. 1348-1351, Apr. 1987.
[17] R. Martin, “An efficient algorithm to estimate the instantaneous SNR of speech signals,” in Proc. Eur. Signal Processing Conf., pp. 1093-1096, 1993.
[18] R. Martin, “Spectral subtraction based on minimum statistics,” in Proc. Eur. Signal Processing Conf., pp.1182-1185, 1994.
[19] R. Martin, “Noise power spectral density estimation based on optimal smoothing and minimum statistics,” IEEE Trans. Speech and Audio Processing, vol. 9, no. 5, pp. 504-512, Jul. 2001.
[20] I. Cohen and B. Berdugo, “Noise estimation by minima controlled recursive averaging for robust speech enhancement,” IEEE Signal Processing Letters, vol. 9, no. 1, pp. 12-15, Jan. 2002.
[21] H. Lord, W. S. Gatley, and H. A. Evensen, Noise Control for Engineers, McGraw Hill, 1980, Chapter 2.
[22] J. D. Johnston, “Transform coding of audio signal using perceptual noise criteria,” IEEE J. Select Areas Commun., vol. 6, pp. 314-323, Feb. 1988.
[23] T. Painter and A. Spanias, “Perceptual coding of digital audio,” in Proc. IEEE, vol. 88, no. 4, pp. 451-515, Apr. 2000.
[24] W. M. Fisher, G. R. Doddington, and K. M. Goudie-Marshall, “The DARPA speech recognition research database: specifications and status,” in Proceedings of the DARPA Speech Recognition Workshop, pp. 93-99, Feb. 1986.
[25] A. Varga, H. J. M. Steeneken, M. Tomlinson, and D. Jones, “The NOISEX-92 study on the effect of additive noise automatic speech recognition,” in Description of RSG. 10 and Esprit SAM Experiment and Database, Malvern, U.K.: DRA Speech Res., 1992.
[26] D. Pearce and H. Hirsch, “The AURORA experimental framework for the performance evaluation of speech recognition systems under noisy conditions,” in Proc. ICSLP, pp. 29-32, Oct. 2000.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊