Channel Sparsity-Aware Diagonal Structure Volterra Filters for Nonlinear Acoustic Echo Cancellation

In this paper, we propose a channel sparsity-aware recursive least square (RLS) algorithm using sequential update for nonlinear acoustic echo cancellation (NAEC). The acoustic nonlinear echo path is modeled using the diagonal channel structure Volterra filters. The results are compared with the linear and functional-link artificial neural network (FLANN) filters. The improved performance is validated via computer simulations. The nonlinear echo canceller designed using a third-order Volterra filter offers the best echo cancellation performance while the canceller using the sparse third-order Volterra filter provides the compromised performance with a reduced computational load.

[1]  Jan Skoglund,et al.  Practically efficient nonlinear acoustic echo cancellers using cascaded block RLS and FLMS adaptive filters , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Alfred O. Hero,et al.  Sparse LMS for system identification , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

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

[4]  V. J. O H N M A T H Adaptive Polynomial Filters , 2022 .

[5]  Li Tan,et al.  Adaptive second-order volterra RLS algorithms with dynamic selection of channel updates , 2010, 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[6]  Paulo S. R. Diniz,et al.  Recursive Least-Squares algorithms for sparse system modeling , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

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

[9]  Li Tan,et al.  Channel sparsity-aware recursive least squares algorithms for nonlinear system modeling and active noise control , 2017, 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON).

[10]  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).

[11]  Li Tan Adaptive function expansion RLS filters with dynamic selection of channel updates for nonlinear active noise control , 2010, 2010 International Conference on Intelligent Control and Information Processing.

[12]  Li Tan,et al.  An adaptive technique for modeling second-order Volterra systems with sparse kernels , 1998 .

[13]  Li Tan,et al.  Adaptive second-order Volterra delay filter , 1996 .

[14]  Li Tan,et al.  Adaptive second-order Volterra filtered-X RLS algorithms with sequential and partial updates for nonlinear active noise control , 2009, 2009 4th IEEE Conference on Industrial Electronics and Applications.

[15]  Yang Li,et al.  Sparse Modeling of Nonlinear Secondary Path for Nonlinear Active Noise Control , 2018, IEEE Transactions on Instrumentation and Measurement.

[16]  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.

[17]  Danilo Comminiello,et al.  Full proportionate functional link adaptive filters for nonlinear acoustic echo cancellation , 2017, 2017 25th European Signal Processing Conference (EUSIPCO).