Transient and steady-state analysis of filtered-x affine projection algorithms with imperfect secondary path estimate

This paper provides an analysis based on energy conservation arguments of the transient and steady-state behaviors of two filtered-x affine projection algorithms in presence of an imperfect estimation of the secondary paths of an active noise control system. Very mild assumptions are posed on the system model, which is only required to have a linear dependence of the output from the filter coefficients.

[1]  Ali H. Sayed,et al.  Mean-square performance of a family of affine projection algorithms , 2004, IEEE Transactions on Signal Processing.

[2]  Scott D. Snyder,et al.  The effect of transfer function estimation errors on the filtered-x LMS algorithm , 1994, IEEE Trans. Signal Process..

[3]  Paulo Sergio Ramirez,et al.  Fundamentals of Adaptive Filtering , 2002 .

[4]  Pierre Chapelle,et al.  Active noise control with dynamic recurrent neural networks , 1995, ESANN.

[5]  Elias Bjarnason Analysis of the filtered-X LMS algorithm , 1995, IEEE Trans. Speech Audio Process..

[6]  Martin Bouchard,et al.  Improved training of neural networks for the nonlinear active control of sound and vibration , 1999, IEEE Trans. Neural Networks.

[7]  A. A. Beex,et al.  Convergence behavior of affine projection algorithms , 2000, IEEE Trans. Signal Process..

[8]  J. Mazo On the independence theory of equalizer convergence , 1979, The Bell System Technical Journal.

[9]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[10]  Stephen J. Elliott,et al.  A multiple error LMS algorithm and its application to the active control of sound and vibration , 1987, IEEE Trans. Acoust. Speech Signal Process..

[11]  Jean Jiang,et al.  Filtered-X second-order Volterra adaptive algorithms , 1997 .

[12]  Philip A. Nelson,et al.  Active Control of Sound , 1992 .

[13]  Paul Strauch,et al.  Active control of nonlinear noise processes in a linear duct , 1998, IEEE Trans. Signal Process..

[14]  Joao M. C. Sousa,et al.  ACTIVE NOISE CONTROL BASED ON FUZZY MODELS , 2000 .

[15]  H. Brehm,et al.  Description and generation of spherically invariant speech-model signals , 1987 .

[16]  V. J. Mathews,et al.  Polynomial Signal Processing , 2000 .

[17]  H. Itoh,et al.  Active noise control by using prediction of time series data with a neural network , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[18]  Giovanni L. Sicuranza,et al.  Filtered-X Affine Projection Algorithms for Active Noise Control Using Volterra Filters , 2004, EURASIP J. Adv. Signal Process..

[19]  Rui Seara,et al.  Performance comparison of the FXLMS, nonlinear FXLMS and leaky FXLMS algorithms in nonlinear active control applications , 2002, 2002 11th European Signal Processing Conference.

[20]  Li Tan,et al.  Adaptive Volterra filters for active control of nonlinear noise processes , 2001, IEEE Trans. Signal Process..

[21]  Giovanni L. Sicuranza,et al.  Steady-state and transient analysis of multichannel filtered-x affine projection algorithms , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[22]  Ganapati Panda,et al.  Active mitigation of nonlinear noise Processes using a novel filtered-s LMS algorithm , 2004, IEEE Transactions on Speech and Audio Processing.

[23]  Giovanni L. Sicuranza,et al.  Filtered-X affine projection algorithm for multichannel active noise control using second-order Volterra filters , 2004, IEEE Signal Processing Letters.

[24]  Steve McLaughlin,et al.  A stochastic analysis of the affine projection algorithm for Gaussian autoregressive inputs , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[25]  Rui Seara,et al.  Leaky delayed LMS algorithm: stochastic analysis for Gaussian data and delay modeling error , 2004, IEEE Transactions on Signal Processing.

[26]  José Carlos M. Bermudez,et al.  Mean weight behavior of the filtered-X LMS algorithm , 2000, IEEE Trans. Signal Process..

[27]  Martin Bouchard,et al.  Multichannel recursive-least-square algorithms and fast-transversal-filter algorithms for active noise control and sound reproduction systems , 2000, IEEE Trans. Speech Audio Process..

[28]  José Carlos M. Bermudez,et al.  Stochastic analysis of the filtered-X LMS algorithm in systems with nonlinear secondary paths , 2002, IEEE Trans. Signal Process..

[29]  Gerhard Schmidt,et al.  Acoustic echo control. An application of very-high-order adaptive filters , 1999, IEEE Signal Process. Mag..

[30]  Masato Miyoshi,et al.  Inverse filtering of room acoustics , 1988, IEEE Trans. Acoust. Speech Signal Process..

[31]  José Antonio Apolinário,et al.  Convergence analysis of the binormalized data-reusing LMS algorithm , 2000, IEEE Trans. Signal Process..

[32]  Tareq Y. Al-Naffouri,et al.  Transient analysis of data-normalized adaptive filters , 2003, IEEE Trans. Signal Process..

[33]  S. Douglas The fast affine projection algorithm for active noise control , 1995, Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers.

[34]  Martin Bouchard Multichannel affine and fast affine projection algorithms for active noise control and acoustic equalization systems , 2003, IEEE Trans. Speech Audio Process..