Convex Combination of Overlap-Save Frequency-Domain Adaptive Filters

In order to decrease the steady-state error and reduce the computational complexity and increase the ability to identify a large unknown system, a convex combination of overlap-save frequency-domain adaptive filters (COSFDAF) algorithm is proposed. From the articles available, most papers discuss convex combinations of adaptive-filter algorithms focusing on the time domain. Those algorithms show better performances in convergence speed and steady-state error. The major defect of those algorithms, however, is the computational complexity. To deal with this problem and motivated by frequency-domain adaptive filters (FDAF) and convex optimization, this paper gives an adaptive filter algorithm, that consists of combining the two FDAFs using the convex combination principles and derives a formula to update the mixing parameter. The computational complexity of the COSFDAF is analyzed theoretically. The simulation results show that no matter what kinds of signal to be processed, whether correlated (i.e. colored noise) or uncorrelated (i.e. white noise), the proposed algorithm has better performance in identify the unknown coefficients when compared to a single overlap-save FDAF or the convex combination of two time-domain adaptive filters.

[1]  B. Widrow,et al.  Adaptive filtering in the frequency domain , 1978, Proceedings of the IEEE.

[2]  Hun Choi,et al.  Convex combination of subband adaptive filters for sparse impulse response systems , 2011, 2011 IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS).

[3]  J. Shynk.  Frequency-domain and multirate adaptive filtering , 1992, IEEE Signal Processing Magazine.

[4]  A. Gray,et al.  Unconstrained frequency-domain adaptive filter , 1982 .

[5]  Ali H. Sayed,et al.  Adaptive Networks , 2014, Proceedings of the IEEE.

[6]  Ali H. Sayed,et al.  Mean-square performance of a convex combination of two adaptive filters , 2006, IEEE Transactions on Signal Processing.

[7]  Alberto González,et al.  Convex Combination Filtered-X Algorithms for Active Noise Control Systems , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[8]  Hun Choi,et al.  Subband adaptive convex combination of two NLMS based filters for sparse impulse response systems , 2012, 2012 IEEE Statistical Signal Processing Workshop (SSP).

[9]  Junghsi Lee,et al.  On the Step-Size Bounds of Frequency-Domain Block LMS Adaptive Filters , 2013, IEEE Signal Processing Letters.

[10]  Liming Shi,et al.  Convex Combination of Adaptive Filters under the Maximum Correntropy Criterion in Impulsive Interference , 2014, IEEE Signal Processing Letters.

[11]  Jean-Yves Tourneret,et al.  An affine combination of two NLMS adaptive filters - Transient mean-square analysis , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.

[12]  Á. Navia-Vázquez,et al.  An adaptive combination of adaptive filters for plant identification , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[13]  Raymond A. de Callafon,et al.  Detection of Secondary-Path Irregularities in Active Noise Control Headphones , 2014, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[14]  Jacob Benesty,et al.  On Optimal Frequency-Domain Multichannel Linear Filtering for Noise Reduction , 2010, IEEE Transactions on Audio, Speech, and Language Processing.