Kernel-based identification of Hammerstein systems for nonlinear acoustic echo-cancellation

Traditional acoustic echo cancelers use a linear model to represent the echo path. Nevertheless, many consumer devices include loudspeakers and audio power amplifiers that may generate significant nonlinear distortions, creating the need for acoustic echo cancelers to produce a nonlinear filter response. To address this issue, we propose a nonlinear acoustic echo cancellation algorithm based on the framework of kernel methods. We model the echo path as a Hammerstein system, and we propose a resource-efficient strategy to identify the nonlinear and linear parts. While the basic algorithm is presented as an iterative batch method, we show that a simple extension allows it to be used in online scenarios as well. Results for both types of scenarios show that the algorithm produces good results on a system with a clipping nonlinearity and a realistic room impulse response.

[1]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[2]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[3]  Alberto Carini,et al.  Fourier nonlinear filters , 2014, Signal Process..

[4]  Moctar Mossi Idrissa,et al.  An assessment of linear adaptive filter performance with nonlinear distortions , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[5]  Danilo Comminiello,et al.  Functional Link Adaptive Filters for Nonlinear Acoustic Echo Cancellation , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[6]  Kun Shi,et al.  Nonlinear acoustic echo cancellation , 2008 .

[7]  Danilo Comminiello,et al.  Nonlinear spline adaptive filtering , 2013, Signal Process..

[8]  Walter Kellermann,et al.  Nonlinear acoustic echo cancellation using adaptive orthogonalized power filters , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

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

[10]  W. Marsden.  I and J , 2012 .

[11]  Marc Moonen,et al.  Nonlinear Acoustic Echo Cancellation Based on a Sliding-Window Leaky Kernel Affine Projection Algorithm , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[12]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[13]  Aníbal R. Figueiras-Vidal,et al.  Adaptive Combination of Volterra Kernels and Its Application to Nonlinear Acoustic Echo Cancellation , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[14]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[15]  Steven Van Vaerenbergh,et al.  Kernel Methods for Nonlinear Identification, Equalization and Separation of Signals , 2010 .

[16]  M. Tuomo,et al.  EFFECTS OF HARMONIC COMPONENTS GENERATED BY POLYNOMIAL PREPROCESSOR IN ACOUSTIC ECHO CONTROL , 2003 .

[17]  Wei-Ping Zhu,et al.  A Nonlinear Acoustic Echo Canceller Using Sigmoid Transform in Conjunction With RLS Algorithm , 2008, IEEE Transactions on Circuits and Systems II: Express Briefs.

[18]  Christina Breining.  Acoustic echo control , 1999 .

[19]  C. D. Kemp,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[20]  J. Arenas-García,et al.  Functional link based architectures for nonlinear acoustic echo cancellation , 2011, 2011 Joint Workshop on Hands-free Speech Communication and Microphone Arrays.

[21]  Rudolf Rabenstein,et al.  Nonlinear acoustic echo cancellation with 2nd order adaptive Volterra filters , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[22]  Jean Pierre Da Costa,et al.  Acoustic echo cancellation using nonlinear cascade filters , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[23]  Régine Le Bouquin-Jeannès,et al.  Nonlinear acoustic echo cancellation based on Volterra filters , 2003, IEEE Trans. Speech Audio Process..

[24]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[25]  M.A.E. Andersen,et al.  Carrier Distortion in Hysteretic Self-Oscillating Class-D Audio Power Amplifiers: Analysis and Optimization , 2009, IEEE Transactions on Power Electronics.

[26]  Walter Kellermann,et al.  COEFFICIENT PRUNING FOR HIGHER-ORDER DIAGONALS OF VOLTERRA FILTERS REPRESENTING WIENER-HAMMERSTEIN MODELS , 2008 .

[27]  J. Voros.  AN ITERATIVE METHOD FOR HAMMERSTEIN-WIENER SYSTEMS PARAMETER IDENTIFICATION , 2004 .

[28]  Trevor Burton,et al.  Nonlinear System Identification Using a Subband Adaptive Volterra Filter , 2008, 2008 IEEE Instrumentation and Measurement Technology Conference.

[29]  Frédéric Maire,et al.  Implementation of Kernel Methods on the GPU , 2005, Digital Image Computing: Techniques and Applications (DICTA'05).

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

[31]  Walter Kellermann,et al.  Partitioned block frequency-domain adaptive second-order Volterra filter , 2005, IEEE Transactions on Signal Processing.