基于成比例的自适应鲁棒回声消除算法
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摘要
在网络或是声学回声消除中,回声信道的脉冲响应很长且具备稀疏性,因此要用到阶数很长的自适应稀疏滤波器。自适应稀疏滤波器的核心便是成比例算法,成比例类的自适应算法利用了回声路径脉冲响应稀疏的结构特性,在回声消除领域有着广泛的应用。本文围绕回声消除问题和自适应成比例算法进行了详细的分析和研究,并对回声信道建模的方法进行了总结。
本文改进了基于u准则MPNLMS算法的成比例因子计算方法,并采用新型的线性分段函数替代u压缩函数得到了收敛性能更好的改进型分段成比例归一化最小均方算法(ISMPNLMS)。
为了获得抗冲击噪声性能,引入了M-估计的概念,采用Huber范数并结合新型的成比例控制矩阵计算方法得到一类成比例归一化最小均值M-估计算法,包括PNLMM和IPNLMM两种算法。在PNLMM类算法的启发下,采用新型的核函数得到PAPA-M类算法,包括PAPA-M、IPAPA-M和μ-PAPA-M三种算法。在仿真实验中,PNLMM类算法与PAPA-M类算法在冲击噪声环境中均具备良好的收敛性能。此外,引用了Geigel双通话侦测算法,验证了PNLMM类算法与PAPA-M类算法在双通话状态下回声消除的有效性。
Sparse adaptive filters with lots of coeffcients are usually involved in both acoustic and network echo cancellation, because the impulse response of echo path is long and sparse. The core of the sparse adaptive filter is the proportionate-type algorithm which takes advantage of sparsity in impulse response, and has applied in echo cancellation widely. The problem of echo cancellation and proportionate-type algorithm are analysed in this thesis detailedly.
In this thesis, we improved the update rule of proportionate matrix in MPNLMS, and used a new piecewise linear function instead of the logarithmic function, then get the improved segment MPNLMS(ISMPNLMS) has faster converge speed.
In order to get the good performce in an impulsive noise environment, we proposed proportionate-type normalized least mean m-estimate algorithms (PNLMMs) by modified Huber function. Inspired by PNLMMs algorithm, we proposed a novel based M-estimation proportionate-type affine projection algorithm (PAPA-Ms) by a new score function. In the simulation, PNLMMs and PAPA-Ms show the good convergence performance in impulsive noise environments. In addition, verify the effectiveness of the PNLMMs and PAPA-Ms by Geigel double-talk detection during the double-talk.
本文改进了基于u准则MPNLMS算法的成比例因子计算方法,并采用新型的线性分段函数替代u压缩函数得到了收敛性能更好的改进型分段成比例归一化最小均方算法(ISMPNLMS)。
为了获得抗冲击噪声性能,引入了M-估计的概念,采用Huber范数并结合新型的成比例控制矩阵计算方法得到一类成比例归一化最小均值M-估计算法,包括PNLMM和IPNLMM两种算法。在PNLMM类算法的启发下,采用新型的核函数得到PAPA-M类算法,包括PAPA-M、IPAPA-M和μ-PAPA-M三种算法。在仿真实验中,PNLMM类算法与PAPA-M类算法在冲击噪声环境中均具备良好的收敛性能。此外,引用了Geigel双通话侦测算法,验证了PNLMM类算法与PAPA-M类算法在双通话状态下回声消除的有效性。
Sparse adaptive filters with lots of coeffcients are usually involved in both acoustic and network echo cancellation, because the impulse response of echo path is long and sparse. The core of the sparse adaptive filter is the proportionate-type algorithm which takes advantage of sparsity in impulse response, and has applied in echo cancellation widely. The problem of echo cancellation and proportionate-type algorithm are analysed in this thesis detailedly.
In this thesis, we improved the update rule of proportionate matrix in MPNLMS, and used a new piecewise linear function instead of the logarithmic function, then get the improved segment MPNLMS(ISMPNLMS) has faster converge speed.
In order to get the good performce in an impulsive noise environment, we proposed proportionate-type normalized least mean m-estimate algorithms (PNLMMs) by modified Huber function. Inspired by PNLMMs algorithm, we proposed a novel based M-estimation proportionate-type affine projection algorithm (PAPA-Ms) by a new score function. In the simulation, PNLMMs and PAPA-Ms show the good convergence performance in impulsive noise environments. In addition, verify the effectiveness of the PNLMMs and PAPA-Ms by Geigel double-talk detection during the double-talk.
引文
[1]尹浩琼.通信系统中回声消除的研究,北京邮电大学,博士论文.2006
[2]胡立宁.自适应回声消除算法的研究与实现,吉林大学,博士论文.2007.
[3]M.M.Sondhi. An Adaptive Echo Canceler, Bell System Technical Journal,1967,46: 497-510
[4]Ahgren, P., Jakobsson, A. A study of double-talk detection performance in the presence of acoustic echo path changes, Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on,2005,3:141-144
[5]D. L.Duttweiler. Proportionate normalized least-mean-squares adaptation in echo cancelers. IEEE transcation on Speech,Audio Processing, 2000,8:508-518
[6]S. L. Gay. An efficient, fast converging adaptive filter for network echo cancellation, in Proc. of Asilomar, 1998,40:394-398
[7]Hongyang Deng, Dyba, R.A.. Partial update PNLMS algorithm for network echo cancellation, Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on,2009.8:1329-1332
[8]H. Deng and M. Doroslovacki. Improving convergence of the PNLMS algorithm for sparse impulse response identification, IEEE Signal Processing Lett.,2005,12: 181-184
[9]H. Deng and M. Doroslovacki. Proportionate adaptive algorithms for network echo cancellation, IEEE Trans. Signal Processing, 2006.54:1794-1803
[10]F. das Chagas de Souza. O. J.Tobias, R. Seara, and D. R. Morgan. A PNLMS algorithm with individual activation factors, IEEETrans. Signal Processing, 2010.58:2036-2047,
[11]de Souza, F.; Seara, R., Morgan, D. An Enhanced IAF-PNLMS Adaptive Algorithm for Sparse Impulse Response Identification, Signal Processing, IEEE Transactions on, 2012,60:3301-3307
[12]J. Benesty and S. L. Gay. An improved PNLMS algorithm, in Proc. IEEE ICASSP, 2002.1881-1884.
[13]C.Paleologu, J. Benesty, S. Ciochina. An improved proportionate NLM Salgorithm based on the l0 norm, in Proc. IEEE ICASSP, 2010.309-312.
[14]Loganathan, P., Khong, A.W.H., Naylor, P.A.. A Class of Sparseness-Controlled Algorithms for Echo Cancellation, Audio, Speech, and Language Processing, IEEE Transactions on,2009.17(8):1591-1601.
[15]L. Liu, M. Fukumoto, S. Zhang. A variable parameter improved proportionate normalized LMS algorithm, in Proc. IEEE APCCAS,2008.201-204.
[16]T. Gaensler, S. L. Gay, M. M. Sondhi, J. Benesty. Double-talk robust fast converging algorithms for network echo cancellation, IEEE Trans. Speech, Audio Processing, 2000.8:656-663.
[17]C. Paleologu, S. Ciochina, J. Benesty. An efficient proportionate affine projection algorithm for echo cancellation, IEEE Signal Processing Lett.,2010.17:165-168.
[18]L. R. Vega,H.Rey, J.Benesty, and S. Tressens. A new robust variable step-size NLMS algorithm, IEEE Trans. Signal Process.,2008.56(5):1878-1893.
[19]T. Shao, Y. R. Zheng, and J. Benesty. An affine projection sign algorithm robust against impulsive interferences, IEEE Signal Process. Lett.,2010.17(4):327-330.
[20]Zengli Yang, Zheng, Y.R., Grant, S.L.. Proportionate affine projection sign algorithms for sparse system identification in impulsive interference, Acoustics, Speech and Signal Processing (ICASSP),2011 IEEE International Conference on.2011,4068-4071.
[21]Zengli Yang, Zheng, Y.R., Grant, S.L.. Proportionate Affine Projection Sign Algorithms for Network Echo Cancellation, Audio, Speech, and Language Processing, IEEE Transactions on,2011.19(8):2273-2284.
[22]J. B. Evans, P. Xue, and B. Liu. Analysis and implementation of variable step size adaptive algorithms, IEEE Trans. Signal Processing, 1993.41:2517-2535.
[23]R. W. Harris, D. M. Chabries, and F. A. Bishop. A variable step adaptive filter algorithm, IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-34,1986. 309-316.
[24]A. Mader, H. Puder, and G. U. Schmidt. Step-size control for acoustic echo cancellation filters - An overview, Signal Processing, 2000.80:1697-1719.
[25]Mahale, P.M.B.. SVSPNLMS Algorithm for Acoustic Echo Cancellation, Information and Communication Technologies:From Theory to Applications, 2008. ICTTA 2008.3rd International Conference on, 2008.7-11
[26]C. Paleologu, J. Benesty, and S. Ciochina. A variable step-size affine projection algorithm designed for acoustic echo cancellation, IEEE Trans. Audio, Speech, Language Processing, 2008.16:1466-1478.
[27]J. Kivinen and M. K. Warmuth. Exponentiated gradient versus gradient descent for linear predictors, Inform. Comput.,1997.132:1-64
[28]S. I. Hill and R. C.Williamson. Convergence of exponentiated gradient algorithms, IEEE Trans. Signal Processing,2001.49:1208-1215.
[29]J. Benesty, T. Gansler, D. R. Morgan, M. M. Sondhi, and S. L. Gay. Advances in Network and Acoustic Echo Cancellation. Berlin, Germany: Springer-Verlag,2001.
[30]E. Hansler and G. Schmidt, Acoustic Echo and Noise Control-A Practical Approach. Hoboken, NJ:Wiley, 2004.
[31]Allen, J. B. and Berkley, D. A. Image method for efficiently simulating small-room acoustics, JASA.1979.65(4),943-950.
[32]N. Wiener. Extrapolation, Interpolation, and Smoothing of Stationary Time Series. New York:John Wiley & Sons, 1949.
[33]S. Haykin. Adaptive Filter Theory. Fourth Edition, Upper Saddle River, NJ: Prentice-Hall,2002.
[34]Y.Huang, J. Benesty,and J.Chen. AcousticMIMOSignal Processing. Berlin, Germany: Springer-Verlag,2006.
[35]J. Homer, I. Mareels, R. R. Bitmead, B. Wahlberg, and A. Gustafsson. LMS estimation via structural detection, IEEE Trans. Signal Processing, 1998.46:2651-2663
[36]T. I. Haweel and P. M. Clarkson. A class of order statistic LMS algorithms, IEEE Trans. Signal Processing,1992.40(1):44-53.
[37]R. Settineri, M. Najim, and D. Ottaviani. Order statistic fast Kalman filter, in Proc. IEEE Int. Symp. Circuits and Systems (ISCAS'96).1996,2:116-119.
[38]S. Koike. Adaptive threshold nonlinear algorithm for adaptive filters with robustness against impulsive noise, IEEE Trans. Signal Processing,1997.45(9):2391-2395.
[39]Huber. P. J. Robust Statistics, Wiley New York,1981.
[40]Y. Zhou, S. C. Chan, and T. S. Ng. A recursive least M-estimate (RLM) adaptive filter for robust filtering in impulsive noise, IEEE Signal Processing Lett.,2000.7:324-326
[41]Y. Zhou, S. C. Chan, and T. S. Ng. A robust M-estimate adaptive filter for impulse noise suppression, in Proc. Int. Conf. Acoustic Speech Signal Processing (ICASSP'99),1999.4:1765-1768.
[42]P. J. Rousseeuw and A. M. Leroy. Robust Regression and Outlier Detection. New York: Wiley, 1987.
[43]R. E. Frank and M. Hampel. Robust Statistics: The Approach Based on Influence Functions. New York:Wiley, 1986.
[44]Y. Zhou, S. C. Chan, and T. S. Ng. Least mean M-estimate algorithms for robust adaptive filtering in impulsive noise, IEEE Trans. Circuits Syst. II,2000.47(12):1564-1569, Dec.2000.
[45]Chan, S.C., Zhou, Y. On the Convergence Analysis of the Normalized LMS and the Normalized Least Mean M-Estimate Algorithms, Signal Processing and Information Technology, 2007 IEEE International Symposium on,2007.1048-1053.
[46]Zhihuan Song, Yanchen Gao, Lihua Lu. A robust adaptive FIR filter based on M-estimation, Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on,2002.2:1543-1546.
[47]D. L. Duttweiler. A twelve-channel digital echo canceler, IEEE Trans.Commun., 1978.26:647-653
[48]T. Gansler, S. Gay, M. Sondhi, and J. Benesty. Double-talk robust fast converging algorithms for network echo cancellation, IEEE Trans. Speech Audio Process.,2000. 8(6):656-663.
[49]K.Ozeki and T. Umeda. An adaptive filtering algorithm using an orthogonal projection to an affine subspace and its properties, Electron. Commun. Jpn. 1984.67:19-27.
[50]T. Shao, Y. R. Zheng, and J. Benesty. An affine projection sign algorithm robust against impulsive interferences, IEEE Signal Process. Lett.,2010.17:327-330.
[2]胡立宁.自适应回声消除算法的研究与实现,吉林大学,博士论文.2007.
[3]M.M.Sondhi. An Adaptive Echo Canceler, Bell System Technical Journal,1967,46: 497-510
[4]Ahgren, P., Jakobsson, A. A study of double-talk detection performance in the presence of acoustic echo path changes, Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on,2005,3:141-144
[5]D. L.Duttweiler. Proportionate normalized least-mean-squares adaptation in echo cancelers. IEEE transcation on Speech,Audio Processing, 2000,8:508-518
[6]S. L. Gay. An efficient, fast converging adaptive filter for network echo cancellation, in Proc. of Asilomar, 1998,40:394-398
[7]Hongyang Deng, Dyba, R.A.. Partial update PNLMS algorithm for network echo cancellation, Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on,2009.8:1329-1332
[8]H. Deng and M. Doroslovacki. Improving convergence of the PNLMS algorithm for sparse impulse response identification, IEEE Signal Processing Lett.,2005,12: 181-184
[9]H. Deng and M. Doroslovacki. Proportionate adaptive algorithms for network echo cancellation, IEEE Trans. Signal Processing, 2006.54:1794-1803
[10]F. das Chagas de Souza. O. J.Tobias, R. Seara, and D. R. Morgan. A PNLMS algorithm with individual activation factors, IEEETrans. Signal Processing, 2010.58:2036-2047,
[11]de Souza, F.; Seara, R., Morgan, D. An Enhanced IAF-PNLMS Adaptive Algorithm for Sparse Impulse Response Identification, Signal Processing, IEEE Transactions on, 2012,60:3301-3307
[12]J. Benesty and S. L. Gay. An improved PNLMS algorithm, in Proc. IEEE ICASSP, 2002.1881-1884.
[13]C.Paleologu, J. Benesty, S. Ciochina. An improved proportionate NLM Salgorithm based on the l0 norm, in Proc. IEEE ICASSP, 2010.309-312.
[14]Loganathan, P., Khong, A.W.H., Naylor, P.A.. A Class of Sparseness-Controlled Algorithms for Echo Cancellation, Audio, Speech, and Language Processing, IEEE Transactions on,2009.17(8):1591-1601.
[15]L. Liu, M. Fukumoto, S. Zhang. A variable parameter improved proportionate normalized LMS algorithm, in Proc. IEEE APCCAS,2008.201-204.
[16]T. Gaensler, S. L. Gay, M. M. Sondhi, J. Benesty. Double-talk robust fast converging algorithms for network echo cancellation, IEEE Trans. Speech, Audio Processing, 2000.8:656-663.
[17]C. Paleologu, S. Ciochina, J. Benesty. An efficient proportionate affine projection algorithm for echo cancellation, IEEE Signal Processing Lett.,2010.17:165-168.
[18]L. R. Vega,H.Rey, J.Benesty, and S. Tressens. A new robust variable step-size NLMS algorithm, IEEE Trans. Signal Process.,2008.56(5):1878-1893.
[19]T. Shao, Y. R. Zheng, and J. Benesty. An affine projection sign algorithm robust against impulsive interferences, IEEE Signal Process. Lett.,2010.17(4):327-330.
[20]Zengli Yang, Zheng, Y.R., Grant, S.L.. Proportionate affine projection sign algorithms for sparse system identification in impulsive interference, Acoustics, Speech and Signal Processing (ICASSP),2011 IEEE International Conference on.2011,4068-4071.
[21]Zengli Yang, Zheng, Y.R., Grant, S.L.. Proportionate Affine Projection Sign Algorithms for Network Echo Cancellation, Audio, Speech, and Language Processing, IEEE Transactions on,2011.19(8):2273-2284.
[22]J. B. Evans, P. Xue, and B. Liu. Analysis and implementation of variable step size adaptive algorithms, IEEE Trans. Signal Processing, 1993.41:2517-2535.
[23]R. W. Harris, D. M. Chabries, and F. A. Bishop. A variable step adaptive filter algorithm, IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-34,1986. 309-316.
[24]A. Mader, H. Puder, and G. U. Schmidt. Step-size control for acoustic echo cancellation filters - An overview, Signal Processing, 2000.80:1697-1719.
[25]Mahale, P.M.B.. SVSPNLMS Algorithm for Acoustic Echo Cancellation, Information and Communication Technologies:From Theory to Applications, 2008. ICTTA 2008.3rd International Conference on, 2008.7-11
[26]C. Paleologu, J. Benesty, and S. Ciochina. A variable step-size affine projection algorithm designed for acoustic echo cancellation, IEEE Trans. Audio, Speech, Language Processing, 2008.16:1466-1478.
[27]J. Kivinen and M. K. Warmuth. Exponentiated gradient versus gradient descent for linear predictors, Inform. Comput.,1997.132:1-64
[28]S. I. Hill and R. C.Williamson. Convergence of exponentiated gradient algorithms, IEEE Trans. Signal Processing,2001.49:1208-1215.
[29]J. Benesty, T. Gansler, D. R. Morgan, M. M. Sondhi, and S. L. Gay. Advances in Network and Acoustic Echo Cancellation. Berlin, Germany: Springer-Verlag,2001.
[30]E. Hansler and G. Schmidt, Acoustic Echo and Noise Control-A Practical Approach. Hoboken, NJ:Wiley, 2004.
[31]Allen, J. B. and Berkley, D. A. Image method for efficiently simulating small-room acoustics, JASA.1979.65(4),943-950.
[32]N. Wiener. Extrapolation, Interpolation, and Smoothing of Stationary Time Series. New York:John Wiley & Sons, 1949.
[33]S. Haykin. Adaptive Filter Theory. Fourth Edition, Upper Saddle River, NJ: Prentice-Hall,2002.
[34]Y.Huang, J. Benesty,and J.Chen. AcousticMIMOSignal Processing. Berlin, Germany: Springer-Verlag,2006.
[35]J. Homer, I. Mareels, R. R. Bitmead, B. Wahlberg, and A. Gustafsson. LMS estimation via structural detection, IEEE Trans. Signal Processing, 1998.46:2651-2663
[36]T. I. Haweel and P. M. Clarkson. A class of order statistic LMS algorithms, IEEE Trans. Signal Processing,1992.40(1):44-53.
[37]R. Settineri, M. Najim, and D. Ottaviani. Order statistic fast Kalman filter, in Proc. IEEE Int. Symp. Circuits and Systems (ISCAS'96).1996,2:116-119.
[38]S. Koike. Adaptive threshold nonlinear algorithm for adaptive filters with robustness against impulsive noise, IEEE Trans. Signal Processing,1997.45(9):2391-2395.
[39]Huber. P. J. Robust Statistics, Wiley New York,1981.
[40]Y. Zhou, S. C. Chan, and T. S. Ng. A recursive least M-estimate (RLM) adaptive filter for robust filtering in impulsive noise, IEEE Signal Processing Lett.,2000.7:324-326
[41]Y. Zhou, S. C. Chan, and T. S. Ng. A robust M-estimate adaptive filter for impulse noise suppression, in Proc. Int. Conf. Acoustic Speech Signal Processing (ICASSP'99),1999.4:1765-1768.
[42]P. J. Rousseeuw and A. M. Leroy. Robust Regression and Outlier Detection. New York: Wiley, 1987.
[43]R. E. Frank and M. Hampel. Robust Statistics: The Approach Based on Influence Functions. New York:Wiley, 1986.
[44]Y. Zhou, S. C. Chan, and T. S. Ng. Least mean M-estimate algorithms for robust adaptive filtering in impulsive noise, IEEE Trans. Circuits Syst. II,2000.47(12):1564-1569, Dec.2000.
[45]Chan, S.C., Zhou, Y. On the Convergence Analysis of the Normalized LMS and the Normalized Least Mean M-Estimate Algorithms, Signal Processing and Information Technology, 2007 IEEE International Symposium on,2007.1048-1053.
[46]Zhihuan Song, Yanchen Gao, Lihua Lu. A robust adaptive FIR filter based on M-estimation, Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on,2002.2:1543-1546.
[47]D. L. Duttweiler. A twelve-channel digital echo canceler, IEEE Trans.Commun., 1978.26:647-653
[48]T. Gansler, S. Gay, M. Sondhi, and J. Benesty. Double-talk robust fast converging algorithms for network echo cancellation, IEEE Trans. Speech Audio Process.,2000. 8(6):656-663.
[49]K.Ozeki and T. Umeda. An adaptive filtering algorithm using an orthogonal projection to an affine subspace and its properties, Electron. Commun. Jpn. 1984.67:19-27.
[50]T. Shao, Y. R. Zheng, and J. Benesty. An affine projection sign algorithm robust against impulsive interferences, IEEE Signal Process. Lett.,2010.17:327-330.