Abstract
The use of adaptive algorithms for blind system identification in speech dereverberation was proposed recently. This chapter reviews adaptive multichannel system identification using minimization of the cross-relation error. One of the algorithms that adopt this approach is the Normalized Multichannel Frequency Domain Least Mean Square (NMCFLMS) algorithm. We show that, in the presence of additive noise, the coefficients of the adaptive filter employing NMCFLMS converge initially toward the true acoustic impulse responses after which they then misconverge. We provide a technique to address this misconvergence problem in NMCFLMS. This is achieved by reformulating the minimization problem into one involving a constraint. As will be shown, this constrained minimization problem requires knowledge of the direct-path components of the acoustic impulse responses and one of the main contributions of this work is to illustrate how these direct-path components can be estimated under practical conditions.We will then illustrate how these estimates can be incorporated into the proposed extended NMCFLMS (ext-NMCFLMS) algorithm so as to address the problem of misconvergence. The simulation results presented showthe noise robustness of the proposed algorithm for both white Gaussian noise and speech inputs. In addition, we illustrate how errors due to the estimation of the direct-paths affect the performance of the proposed algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Abed-Meraim, K., Qiu, W., Hua, Y.: Blind system identification. Proc. IEEE 85(8), 1310–1322 (1997)
Ahmad, R., Khong, A.W.H., Hasan, M.K., Naylor, P.A.: The extended normalized multichannel FLMS algorithm for blind channel identification. In: Proc. European Signal Processing Conf. (EUSIPCO) (2006)
Ahmad, R., Khong, A.W.H., Naylor, P.A.: Proportionate frequency domain adaptive algorithms for blind channel identification. In: Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), vol. 5 (2006)
Allen, J.B., Berkley, D.A.: Image method for efficiently simulating small-room acoustics. J. Acoust. Soc. Am. 65(4), 943–950 (1979)
Benesty, J., Chen, J., Huang, Y.: Time-delay estimation via linear interpolation and cross correlation. IEEE Trans. Speech Audio Process. 12(5), 509–519 (2004)
Benesty, J., Gänsler, T., Morgan, D.R., Sondhi, M.M., Gay, S.L.: Advances in network and acoustic echo cancellation. Springer (2001)
Benesty, J., Gänsler, T., Morgan, D.R., Sondhi, M.M., Gay, S.L.: General derivation of frequency-domain adaptive filtering. In: Advances in network and acoustic echo cancellation, chap. 8, pp. 157–176. Springer (2001)
Benesty, J., Gay, S.L.: An improved PNLMS algorithm. In: Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), vol. 2, pp. 1881–1884 (2002)
Brandstein, M.S., Silverman, H.F.: A robust method for speech signal time-delay estimation in reverberant rooms. In: Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), vol. 1, pp. 375–378 (1997)
Cadzow, J.A.: Blind deconvolution via cumulant extrema. IEEE Signal Process. Mag. 13(6), 24–42 (1996)
Carter, G., Nuttall, A., Cable, P.: The smoothed coherence transform. Proc. IEEE 61(10), 1497–1498 (1973)
Elko, G.W., Diethorn, E., Gänsler, T.: Room impulse response variation due to thermal fluctuation and its impact on acoustic echo cancellation. In: Proc. Int. Workshop Acoust. Echo Noise Control (IWAENC), pp. 67–70 (2003)
Ferrara, E.R.: Fast implementations of LMS adaptive filters. IEEE Trans. Acoust., Speech, Signal Process. 28(4), 474–475 (1980)
Gannot, S., Moonen, M.: Subspace methods for multi-microphone speech dereverberation. EURASIP J. on App. Signal Process. 2003(11), 1074–1090 (2003)
Golub, G.H., van Loan, C.F.: Matrix computations. Johns Hopkins Univ. Press (1990)
Hannan, E.J., Thomson, P.J.: Estimating group delay. Biometrika 60(2), 241–253 (1973)
Hasan, M.K., Benesty, J., Naylor, P.A., Ward, D.B.: Improving robustness of blind adaptive multichannel identification algorithms using constraints. In: Proc. European Signal Processing Conf. (EUSIPCO) (2005)
Hasan, M.K., Naylor, P.A.: Analyzing effect of noise on LMS-type approaches to blind estimation of SIMO channels: robustness issue. In: Proc. European Signal Processing Conf. (EUSIPCO) (2006)
Hassab, J., Boucher, R.: Performance of the generalized cross correlator in the presence of a strong spectral peak in the signal. IEEE Trans. Acoust., Speech, Signal Process. 29(3), 549–555 (1981)
Haykin, S.: Adaptive filter theory, 4th edn. Information and System Science. Prentice Hall (2002)
Huang, Y., Benesty, J.: Adaptive multi-channel least mean square and newton algorithms for blind channel identification. Signal Processing 82(8), 1127–1138 (2002)
Huang, Y., Benesty, J.: A class of frequency-domain adaptive approaches to blind multichannel identification. IEEE Trans. Signal Process. 51(1), 11–24 (2003)
Huang, Y., Benesty, J., Chen, J.: Optimal step size of the adaptive multichannel lms algorithm for blind SIMO identification. IEEE Signal Process. Lett. 12(3), 173–176 (2005)
Inouye, Y., Hirano, K.: Cumulant-based blind identification of linear multi-input-multi-output systems driven by colored inputs. IEEE Trans. Acoust., Speech, Signal Process. 45(6), 1543–1552 (1997)
Khong, A.W.H., Lin, X.S., Naylor, P.A.: Algorithms for identifying clusters of near-common zeros in multichannel blind system identification and equalization. In: Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 389–392 (2008)
Kimura, T., Sasaki, H., Ochi, H.: Blind channel identification using RLS method based on second-order statistics. In: Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), vol. 4, pp. 1785 – 1788 (1999)
Knapp, C., Carter, G.: The generalized correlation method for estimation of time delay. IEEE Trans. Acoust., Speech, Signal Process. 24(4), 320–327 (1976)
Kuttruff, H.: Room acoustics, 4th edn. Taylor and Francis (2000)
Lee, O., Son, Y., Kim, K.: Underwater digital communication using acoustic channel estimation. In: Proc. Oceans, vol. 4, pp. 2453–2456 (2002)
Luo, H., Li, Y.: The application of blind channel identification techniques to prestack seismic deconvolution. Proc. IEEE 86(10), 2082–2089 (1998)
Moon, T., Stirling, W.C.: Theory of constrained optimization. In: Mathematical methods and algorithms for signal processing, chap. 18, pp. 751–786. Prentice Hall (2000)
Morgan, D.R., Benesty, J., Sondhi, M.M.: On the evaluation of estimated impulse responses. IEEE Signal Process. Lett. 5(7), 174–176 (1998)
Moulines, E., Duhamel, P., Cardoso, J.F., Mayrargue, S.: Subspace methods for the blind identification of multichannel FIR filters. IEEE Trans. Signal Process. 43(2), 516–525 (1995)
Ochi, H., Oshiro, M.: Poly-phase based blind deconvolution technique using second-order statistics. In: Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), vol. 3, pp. 1841–1844 (1998)
Perez-Iglesias, H.J., Dapena, A., Castedo, L., Zarzoso, V.: Blind channel identification for Alamoutis coding systems based on eigenvector decomposition. In: Proc. European Wireless (2007)
Roth, P.R.: Effective measurements using digital signal analysis. IEEE Spectr. 8, 62–70 (1971)
Shynk, J.J.: Frequency-domain and multirate adaptive filtering. IEEE Signal Process. Mag. 9(1), 14–37 (1992)
Soo, J.S., Pang, K.K.: Multidelay block frequency domain adaptive filter. IEEE Trans. Acoust., Speech, Signal Process. 38(2), 373–376 (1990)
Tong, L., Perreau, S.: Multichannel blind identification: from subspace to maximum likelihood methods. Proc. IEEE 86(10), 1951–1968 (1998)
Tong, L., Xu, G., Kailath, T.: A new approach to blind identification and equalization of multipath channels. In: Proc. Asilomar Conf. on Signals, Systems and Computers, vol. 2, pp. 856–860 (1991)
Tsatsanis, M.K., Giannakis, G.B.: Subspace methods for blind estimation of time-varying FIR channels. IEEE Trans. Acoust., Speech, Signal Process. 45(12), 3084–3093 (1997)
Tugnait, J.K.: A multidelay whitening approach to blind identification and equalization of SIMO channels. IEEE Trans. Wireless Commun. 1(3), 456–467 (2002)
Villavicencio, J., Marquez, L.: Experimental comparison of correlation methods for timedelay estimation. In: Proc. IEEE Int. Conf. Electrical and Electronics Engineering, pp. 433–438 (2004)
Xu, G., Liu, H., Tong, L., Kailath, T.: A least-squares approach to blind channel identification. IEEE Trans. Signal Process. 43(12), 2982–2993 (1995)
Yegnanarayana, B., Prasanna, S., Duraiswami, R., Zotkin, D.: Processing of reverberant speech for time-delay estimation. IEEE Trans. Speech Audio Process. 13(6), 1110–1118 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag London Limited
About this chapter
Cite this chapter
Khong, A., Naylor, P. (2010). Adaptive Blind Multichannel System Identification. In: Naylor, P., Gaubitch, N. (eds) Speech Dereverberation. Signals and Commmunication Technology. Springer, London. https://doi.org/10.1007/978-1-84996-056-4_6
Download citation
DOI: https://doi.org/10.1007/978-1-84996-056-4_6
Publisher Name: Springer, London
Print ISBN: 978-1-84996-055-7
Online ISBN: 978-1-84996-056-4
eBook Packages: EngineeringEngineering (R0)