基于小波变换的语音信号去噪及其DSP算法实现
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摘要
小波分析是上世纪九十年代发展起来的数学分析工具,其优良的时频分析能力使得其在信号处理领域获得了空前的发展,作为小波理论的一个重要分支,近年来小波去噪理论也得到了很大的发展和应用。语音信号去噪是语音信号处理领域中一个重要的组成部分,一般都作为预处理模块存在于系统中。一直以来,人们都在宽带加性噪声的模型上进行研究,提出了各种语音去噪方法,尽管在理论上还没有完全解决语音去噪问题,但是有些方法已经实践被证明是有效的。小波分析由于能同时在时域和频域中对信号进行分析,所以它能有效地实现对语音信号的去噪。
本文研究小波在语音信号去噪方面的应用,重点研究小波阈值语音去噪。目前已经提出的小波去噪方法主要有三种,模极大值去噪、空域相关滤波去噪以及小波阈值去噪法。阈值法具有计算量小、去噪效果好的特点,取得了广泛的应用。然而在阈值法中,阈值的选取直接关系到去噪效果的优劣。如果阈值选取过小,那么一部分噪声小波系数将不能被置零,从而在去噪后的信号中保留了部分噪声信息;如果阈值选的偏大,则会将一部分有用信号去掉,使得去噪后的信号丢失信息。
本文重点研究了小波阈值法,针对不同的阈值函数的选取、阈值处理方法及小波函数的选取做了研究。针对阈值法中高频信号失真的缺点,我们对小尺度上的小波系数做谱减法预处理,之后以一个小阈值去除剩余噪声,大尺度上仍然利用阈值法处理。经过仿真实验表明,这种处理方法较传统的小波阈值法,保留了更多有用信号,减小了去噪后语音信号的失真。
我们将仿真的算法移植到DSP平台中,利用SEED-DEC6713模块,设计实时算法,实现了谱减法、小波阈值法以及我们所提出的方法,验证了仿真效果。
Wavelet analysis is a mathematical analysis tools which was developed in the 1990s, because of its excellent time-frequency analysis ability.Wavelet analysis has undergone a unprecedented development in the signal processing field. As an important branch of wavelet analysis, wavelet de-noising theory has also got a great development and application. Voice signal de-noising is an important area of voice signal processing, generally as a pretreatment module exists in the system. Scholars often carry through study on the basis of broad band plus noise, and has brought forward many voice de-noising methods. Although in theory, still not completely solve the problem of voice de-noising, some methods have been proved to be effective in practice. Wavelet analysis can simultaneously analyse signal in time and frequency domain, so it can effectively achieve the voice signal de-noising.
We study the application of wavelet in voice signal de-noising, focus on the wavelet shrinkage de-noising. There are three main wavelet de-noising methods at present, they are wavelet shrinkage de-noising, model-max de-noising and spatial selection de-noising. wavelet shrinkage de-noising method has a small amount calculation and good de-noising effect, so it has got wide application. But the selection of de-noising threshold directly related to the de-noising effect. Some wavelet coefficients can't be set zero when the threshold is undersize and parts of noises are retained; some useful signals will be reduced if the threshold is up-size. These cases may degrade the de-noising effect.
This article focuses on the wavelet shrinkage de-noising, study different threshold function, different threshold approach and the selection of mother wavelet. For the shortcoming of high frequency signal distortion in the wavelet shrinkage de-noising, we use spectral subtraction to treat with small scales wavelet coefficients and then remove the residual noise with a small threshold; in big scales, we still use threshold method directly. The simulation shows that this method has a better effect than wavelet shrinkage de-noising, it could reserves more high frequency signal, reduce distortion.
We design real time de-noising method, and transplant the above-mentioned algorithms to DSP with SEED-EDC6713 module. We completed spectral subtraction, wavelet shrinkage de-noising and the method this article put forward, verify the simulation results.
本文研究小波在语音信号去噪方面的应用,重点研究小波阈值语音去噪。目前已经提出的小波去噪方法主要有三种,模极大值去噪、空域相关滤波去噪以及小波阈值去噪法。阈值法具有计算量小、去噪效果好的特点,取得了广泛的应用。然而在阈值法中,阈值的选取直接关系到去噪效果的优劣。如果阈值选取过小,那么一部分噪声小波系数将不能被置零,从而在去噪后的信号中保留了部分噪声信息;如果阈值选的偏大,则会将一部分有用信号去掉,使得去噪后的信号丢失信息。
本文重点研究了小波阈值法,针对不同的阈值函数的选取、阈值处理方法及小波函数的选取做了研究。针对阈值法中高频信号失真的缺点,我们对小尺度上的小波系数做谱减法预处理,之后以一个小阈值去除剩余噪声,大尺度上仍然利用阈值法处理。经过仿真实验表明,这种处理方法较传统的小波阈值法,保留了更多有用信号,减小了去噪后语音信号的失真。
我们将仿真的算法移植到DSP平台中,利用SEED-DEC6713模块,设计实时算法,实现了谱减法、小波阈值法以及我们所提出的方法,验证了仿真效果。
Wavelet analysis is a mathematical analysis tools which was developed in the 1990s, because of its excellent time-frequency analysis ability.Wavelet analysis has undergone a unprecedented development in the signal processing field. As an important branch of wavelet analysis, wavelet de-noising theory has also got a great development and application. Voice signal de-noising is an important area of voice signal processing, generally as a pretreatment module exists in the system. Scholars often carry through study on the basis of broad band plus noise, and has brought forward many voice de-noising methods. Although in theory, still not completely solve the problem of voice de-noising, some methods have been proved to be effective in practice. Wavelet analysis can simultaneously analyse signal in time and frequency domain, so it can effectively achieve the voice signal de-noising.
We study the application of wavelet in voice signal de-noising, focus on the wavelet shrinkage de-noising. There are three main wavelet de-noising methods at present, they are wavelet shrinkage de-noising, model-max de-noising and spatial selection de-noising. wavelet shrinkage de-noising method has a small amount calculation and good de-noising effect, so it has got wide application. But the selection of de-noising threshold directly related to the de-noising effect. Some wavelet coefficients can't be set zero when the threshold is undersize and parts of noises are retained; some useful signals will be reduced if the threshold is up-size. These cases may degrade the de-noising effect.
This article focuses on the wavelet shrinkage de-noising, study different threshold function, different threshold approach and the selection of mother wavelet. For the shortcoming of high frequency signal distortion in the wavelet shrinkage de-noising, we use spectral subtraction to treat with small scales wavelet coefficients and then remove the residual noise with a small threshold; in big scales, we still use threshold method directly. The simulation shows that this method has a better effect than wavelet shrinkage de-noising, it could reserves more high frequency signal, reduce distortion.
We design real time de-noising method, and transplant the above-mentioned algorithms to DSP with SEED-EDC6713 module. We completed spectral subtraction, wavelet shrinkage de-noising and the method this article put forward, verify the simulation results.
引文
1.应和民.基于小波变换的语音信号降噪研究.哈尔滨工程大学.2007:2～3
2. J.S.Lim, A.V Oppenheim. All-pole modeling of degraded speech. IEEE Transactions on Acoustic [J]. Speech and Signal Processing,1978, Vol. 26, No.3: 197～210
3. S.F Boll. Suppression of Acoustic Noise in Speech Using Spectral Subtraction [J].IEEE Transactions on Acoustics, Speech and Signal Processing, 1979, Vol. 67, No.3:113～120.
4. R.J.McAulay,M.L.Malpass. Speech enhancement using a soft-decision noise suppression filter [J]. IEEE Transactions on Acoustics, Speech and Signal Processing,1980, Vol. 28, No.2: 137～145.
5. Y Ephraim and D. Malah. Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator [J]. IEEE Transactions on Acoustics, Speech,and SignalProcessing, vol. ASSP-32, no. 6, Dec. 1984, pp. 1109～1121.
6. Y Ephraim and D. Malah. Speech enhancement using a minimum mean square error log-spectral amplitude estimator [J], IEEE Trans. on Acoust. Speech, Signal Processing,vol. ASSP-33, Apr. 1985, pp. 443～445.
7.郝明远.基于小波变换的语音增强研究.吉林大学,2005:2～5
8. Mallat S.A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Machine Inetll. ,1989,11(7):674～693
9.潘泉,张磊,孟晋丽,张洪才.小波滤波方法及应用.清华大学出版社,2005
10. Mallat S. A Wavelet Tour of Signal Processing. California: Academic Press, 2nd Edition,1999
11. Mallat S. Zhong S. Characterization of signals from multiscale edges. IEEE Trans. Pattern Anal. Machine Intell., 1992,14(7):710-723
12.葛哲学,沙威.小波分析理论与MATLAB R2007实现.电子工业出版社,2007:40
13. Mallat S,Hwang W L. Singularity detection and processing with wavelets. IEEE Trans. Inform. Theory,1992,38(2):617～643
14. Witkin A. Scale space filtering. Proc. 8th Int. Jiont Conf. Artificial Intell. 1983
15. Xu Yansun, et al. Wavelet transform domain filters: a spatially selective noise filtration technique. IEEE Trans. Image Processing,1994,3(6):747～758
16. Donoho D L.De-noising by soft-thresholding. IEEE Trans. Inform. Theory, 1995,41(3):613～627
17. Gao Hong-Ye. Wavelet shrinkage denoising using the non-negative garrote. Journal of Computational and Graphical Statistics,1998, 7(4):469～488
18. Zhang Xiao Ping,desai M D. Adaptive denoising based on SURE risk.IEEE signal Processing Letters,1998,5(10):265～267
19.张建良.基于小波变换的语音信号噪声处理研究.兰州大学硕士论文.2004:23～24
20.曲天书,戴逸松,王树勋.基于SURE无偏估计的自适应小波阈值去噪.电子学报,2002,30(2):266～268
21.赵瑞珍.小波理论及其在图像、信号处理中的算法研究.西安:西安电子科技大学博士学位论文,2001
22. Bruce A G,Gao Hong-Ye. Understanding waveShink: variance and bias estimation. Biometrika,1996,83(4):727～745
23. Gao Hong-Ye,Bruce A G. WaveShrink and semisoft shrinkage. StaSci Reshearch Peport NO.39,1995
24. Bruce A G,Gao Hong-Ye.Waveshrink with firm shrinkage. Statistica Sinica,1997,7(4):855～874
25.赵力.语音信号处理.机械工业出版社,2003:27～28
26.刘秀琴.非平稳环境下的谱减法语音增强算法.吉林大学,2005,5
27.林琴,张到信,吴小培.一种基于改进谱减法的语音去噪新方法.2007,17(7)
28.洪晓芬.基于谱减法的改进语音增强方法.计算机工程与设计,2007,28(22)
29.邓克研.基于谱减法的语音增强在DSP环境下实时实现研究.兰州交通大学,2006
30.韩纪庆,张磊,郑铁然.语音信号处理.清华大学出版社,2004:44
31.刘波,聂明心,项俊涛.基于小波分析的阈值语音信号去噪研究.现代计算机,2007.4
32.何超,杨先麟.小波分析在语音信号去噪中的应用.2007,26(8)
33.文莉,刘正士,葛运见.小波去噪的几种方法.合肥工业大学学报,2002,25(2)
34.张吉先,钟秋海,戴亚平.小波门限消噪法应用中分解层数及阈值的确定.中国电机工程学报,2004,24(2)
35.孟晋丽,潘泉,张洪才.基于空域相关的小波硬阈值滤波.计算机工程与应用,2004,27
36. Donoho D L, Johnstone I M. Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association,1995,90:1200～1224
37.董安明.基于DSP的语音增强算法研究.兰州大学硕士学位论文,2007
38.张雄伟,陈亮,杨吉斌.现代语音处理技术及应用.机械工业出版社.2003:248～250
39.芮国胜,康健.小波与傅立叶分析基础.电子工业出版社,2004
40.李方慧,王飞,何佩琨.TMS320C6000系列DSPs原理与应用.电子工业出版社,2005
41.丁玉美,高西全.数字信号处理.第二版.西安电子科技大学,2001
42.胡懂,钱盛友.基于小波变换的语音增强方法研究.计算机工程与应用.2004,43(31)
2. J.S.Lim, A.V Oppenheim. All-pole modeling of degraded speech. IEEE Transactions on Acoustic [J]. Speech and Signal Processing,1978, Vol. 26, No.3: 197～210
3. S.F Boll. Suppression of Acoustic Noise in Speech Using Spectral Subtraction [J].IEEE Transactions on Acoustics, Speech and Signal Processing, 1979, Vol. 67, No.3:113～120.
4. R.J.McAulay,M.L.Malpass. Speech enhancement using a soft-decision noise suppression filter [J]. IEEE Transactions on Acoustics, Speech and Signal Processing,1980, Vol. 28, No.2: 137～145.
5. Y Ephraim and D. Malah. Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator [J]. IEEE Transactions on Acoustics, Speech,and SignalProcessing, vol. ASSP-32, no. 6, Dec. 1984, pp. 1109～1121.
6. Y Ephraim and D. Malah. Speech enhancement using a minimum mean square error log-spectral amplitude estimator [J], IEEE Trans. on Acoust. Speech, Signal Processing,vol. ASSP-33, Apr. 1985, pp. 443～445.
7.郝明远.基于小波变换的语音增强研究.吉林大学,2005:2～5
8. Mallat S.A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Machine Inetll. ,1989,11(7):674～693
9.潘泉,张磊,孟晋丽,张洪才.小波滤波方法及应用.清华大学出版社,2005
10. Mallat S. A Wavelet Tour of Signal Processing. California: Academic Press, 2nd Edition,1999
11. Mallat S. Zhong S. Characterization of signals from multiscale edges. IEEE Trans. Pattern Anal. Machine Intell., 1992,14(7):710-723
12.葛哲学,沙威.小波分析理论与MATLAB R2007实现.电子工业出版社,2007:40
13. Mallat S,Hwang W L. Singularity detection and processing with wavelets. IEEE Trans. Inform. Theory,1992,38(2):617～643
14. Witkin A. Scale space filtering. Proc. 8th Int. Jiont Conf. Artificial Intell. 1983
15. Xu Yansun, et al. Wavelet transform domain filters: a spatially selective noise filtration technique. IEEE Trans. Image Processing,1994,3(6):747～758
16. Donoho D L.De-noising by soft-thresholding. IEEE Trans. Inform. Theory, 1995,41(3):613～627
17. Gao Hong-Ye. Wavelet shrinkage denoising using the non-negative garrote. Journal of Computational and Graphical Statistics,1998, 7(4):469～488
18. Zhang Xiao Ping,desai M D. Adaptive denoising based on SURE risk.IEEE signal Processing Letters,1998,5(10):265～267
19.张建良.基于小波变换的语音信号噪声处理研究.兰州大学硕士论文.2004:23～24
20.曲天书,戴逸松,王树勋.基于SURE无偏估计的自适应小波阈值去噪.电子学报,2002,30(2):266～268
21.赵瑞珍.小波理论及其在图像、信号处理中的算法研究.西安:西安电子科技大学博士学位论文,2001
22. Bruce A G,Gao Hong-Ye. Understanding waveShink: variance and bias estimation. Biometrika,1996,83(4):727～745
23. Gao Hong-Ye,Bruce A G. WaveShrink and semisoft shrinkage. StaSci Reshearch Peport NO.39,1995
24. Bruce A G,Gao Hong-Ye.Waveshrink with firm shrinkage. Statistica Sinica,1997,7(4):855～874
25.赵力.语音信号处理.机械工业出版社,2003:27～28
26.刘秀琴.非平稳环境下的谱减法语音增强算法.吉林大学,2005,5
27.林琴,张到信,吴小培.一种基于改进谱减法的语音去噪新方法.2007,17(7)
28.洪晓芬.基于谱减法的改进语音增强方法.计算机工程与设计,2007,28(22)
29.邓克研.基于谱减法的语音增强在DSP环境下实时实现研究.兰州交通大学,2006
30.韩纪庆,张磊,郑铁然.语音信号处理.清华大学出版社,2004:44
31.刘波,聂明心,项俊涛.基于小波分析的阈值语音信号去噪研究.现代计算机,2007.4
32.何超,杨先麟.小波分析在语音信号去噪中的应用.2007,26(8)
33.文莉,刘正士,葛运见.小波去噪的几种方法.合肥工业大学学报,2002,25(2)
34.张吉先,钟秋海,戴亚平.小波门限消噪法应用中分解层数及阈值的确定.中国电机工程学报,2004,24(2)
35.孟晋丽,潘泉,张洪才.基于空域相关的小波硬阈值滤波.计算机工程与应用,2004,27
36. Donoho D L, Johnstone I M. Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association,1995,90:1200～1224
37.董安明.基于DSP的语音增强算法研究.兰州大学硕士学位论文,2007
38.张雄伟,陈亮,杨吉斌.现代语音处理技术及应用.机械工业出版社.2003:248～250
39.芮国胜,康健.小波与傅立叶分析基础.电子工业出版社,2004
40.李方慧,王飞,何佩琨.TMS320C6000系列DSPs原理与应用.电子工业出版社,2005
41.丁玉美,高西全.数字信号处理.第二版.西安电子科技大学,2001
42.胡懂,钱盛友.基于小波变换的语音增强方法研究.计算机工程与应用.2004,43(31)