Dynamically Regularized Fast RLS with Application to Echo Cancellation

This paper introduces a dynamically regularized fast recursive least squares (DR-FRLS) adaptive filtering algorithm. Numerically stabilized FRLS algorithms exhibit reliable and fast convergence with low complexity even when the excitation signal is highly self-correlated. FRLS still suffers from instability, however, when the condition number of the implicit excitation sample covariance matrix is very high. DR-FRLS, overcomes this problem with a regularization process which only increases the computational complexity by 50%. The benefits of regularization include: (1) the ability to use small forgetting factors resulting in improved tracking ability and (2) better convergence over the standard regularization technique of noise injection. Also, DR-FRLS allows the degree of regularization to be modified quickly without restarting the algorithm. The application of DR-FRLS to stabilizing the fast affine projection (FAR) algorithm is also discussed

Underdetermined-order recursive least-squares adaptive filtering: The concept and algorithms

Published versio

Variable Regularized Fast Affine Projections

This paper introduces a variable regularization method for the fast affine projection algorithm (VR-FAP). It is inspired by a recently introduced technique for variable regularization of the classical, affine projection algorithm (VR-APA). In both algorithms, the regularization parameter varies as a function of the excitation, measurement noise, and residual error energies. Because of the dependence on the last parameter, VR-APA and VR-FAP demonstrate the desirable property of fast convergence (via a small regularization value) when the convergence is poor and deep convergence/immunity to measurement noise (via a large regularization value) when the convergence is good. While the regularization parameter of APA is explicitly available for on-line modification, FAP\u27s regularization is only set at initialization. To overcome this problem we use noise-injection with the noise-power proportional to the variable regularization parameter. As with their fixed regularization versions, VR-FAP is considerably less complex than VR-APA and simulations verify that they have the very similar convergence propertie

Double-Talk Robust Fast Converging Algorithms for Network Echo Cancellation

Echo cancelers which cover longer impulse responses (greater than or equal to 64 ms) are desirable. Long responses create a need for more rapidly converging algorithms in order to meet the specifications for network echo cancelers devised by the ITU (International Telecommunication Union). In general, faster convergence implies a higher sensitivity to near-end disturbances, especially double-talk . Recently, a fast converging algorithm called proportionate NLMS (normalized least mean squares) algorithm (PNLMS) has been proposed. This algorithm exploits the sparseness of the echo path. In this paper we propose a method for making the PNLMS algorithm more robust against double-talk. The slower divergence rate of these robust algorithms in combination with a standard Geigel double-talk detector improves the performance of a network echo canceler considerably during double-talk. This results in the robust PNLMS algorithm which diverges much slower than PNLMS and standard NLMS. A generalization of the robust PNLMS algorithm to a robust proportionate affine projection algorithm (APA) is also presented. It converges very fast, and unlike PNLMS, is not as dependent on the assumption of a sparse echo path response. Trade off between convergence and divergence rate is easily tuned with one parameter and the added complexity is about 7 instructions per sample

Evolutionary and variable step size strategies for multichannel filtered-x affine projection algorithms

This study is focused on the necessity to improve the performance of the affine projection (AP) algorithm for active noise control (ANC) applications. The proposed algorithms are evaluated regarding their steady-state behaviour, their convergence speed and their computational complexity. To this end, different strategies recently applied to the AP for channel identification are proposed for multichannel ANC. These strategies are based either on a variable step size, an evolving projection order, or the combination of both strategies. The developed efficient versions of the AP algorithm use the modified filtered-x structure, which exhibits faster convergence than other filtering schemes. Simulation results show that the proposed approaches exhibit better performance than the conventional AP algorithm and represent a meaningful choice for practical multichannel ANC applications.This work was supported by a grant of the Romanian National Authority for Scientific Research, CNCS-UEFISCDI, project number PN-II-ID-PCE-2011-3-0097, Spanish Ministerio de Ciencia e Innovacion TEC2009-13741 and Generalitat Valenciana PROMETEO 2009/2013.Gonzalez, A.; Albu, F.; Ferrer Contreras, M.; Diego Antón, MD. (2013). Evolutionary and variable step size strategies for multichannel filtered-x affine projection algorithms. IET Signal Processing. 7(6):471-476. https://doi.org/10.1049/iet-spr.2012.0213S47147676Shin, H.-C., Sayed, A. H., & Song, W.-J. (2004). Variable Step-Size NLMS and Affine Projection Algorithms. IEEE Signal Processing Letters, 11(2), 132-135. doi:10.1109/lsp.2003.821722Paleologu, C., Benesty, J., & Ciochina, S. (2008). A Variable Step-Size Affine Projection Algorithm Designed for Acoustic Echo Cancellation. IEEE Transactions on Audio, Speech, and Language Processing, 16(8), 1466-1478. doi:10.1109/tasl.2008.2002980Shin, H.-C., & Sayed, A. H. (2004). Mean-Square Performance of a Family of Affine Projection Algorithms. IEEE Transactions on Signal Processing, 52(1), 90-102. doi:10.1109/tsp.2003.820077Kong, S.-J., Hwang, K.-Y., & Song, W.-J. (2007). An Affine Projection Algorithm With Dynamic Selection of Input Vectors. IEEE Signal Processing Letters, 14(8), 529-532. doi:10.1109/lsp.2007.891325Seong-Eun Kim, Se-Jin Kong, & Woo-Jin Song. (2009). An Affine Projection Algorithm With Evolving Order. IEEE Signal Processing Letters, 16(11), 937-940. doi:10.1109/lsp.2009.2027638Kim, K.-H., Choi, Y.-S., Kim, S.-E., & Song, W.-J. (2011). An Affine Projection Algorithm With Periodically Evolved Update Interval. IEEE Transactions on Circuits and Systems II: Express Briefs, 58(11), 763-767. doi:10.1109/tcsii.2011.2168023Bouchard, M. (2003). Multichannel affine and fast affine projection algorithms for active noise control and acoustic equalization systems. IEEE Transactions on Speech and Audio Processing, 11(1), 54-60. doi:10.1109/tsa.2002.805642Kong, N., Shin, J., & Park, P. (2011). A two-stage affine projection algorithm with mean-square-error-matching step-sizes. Signal Processing, 91(11), 2639-2646. doi:10.1016/j.sigpro.2011.06.003MoonSoo Chang, NamWoong Kong, & PooGyeon Park. (2010). An Affine Projection Algorithm Based on Reuse Time of Input Vectors. IEEE Signal Processing Letters, 17(8), 750-753. doi:10.1109/lsp.2010.2053355Arablouei, R., & Doğançay, K. (2012). Affine projection algorithm with selective projections. Signal Processing, 92(9), 2253-2263. doi:10.1016/j.sigpro.2012.02.018Gonzalez, A., Ferrer, M., de Diego, M., & Piñero, G. (2012). An affine projection algorithm with variable step size and projection order. Digital Signal Processing, 22(4), 586-592. doi:10.1016/j.dsp.2012.03.00

Performance improvement of adaptive filters for echo cancellation applications

This work focuses on performance improvement of adaptive algorithms for both line and acoustic echo cancellation applications. Echo in telephone networks, Line Echo, is observed naturally due to impedance mismatches at the long-distance/local-loop interface. Acoustic echo is due to the acoustic coupling between the microphone and the speaker of a speakerphone. The Affine Projection (APA) and the Fast Affine Projection (FAP) algorithms are two examples of reliable and efficient adaptive filters used for echo cancellation...This thesis presents, Variable Regularized Fast Affine Projections (VR-FAP) algorithm, with a varying, optimal regularization value which provides the desirable property of both fast and low misadjustment of the filter --Abstract, page iii

Active vibration control systems in the frequency and sub-band domain.

Active noise and vibration control has been the subject of intense study in the last two decades due to the increased speed in digital signal processors and the technological development and manufacture of smart materials. This dissertation analyzes an active control system using adaptive digital signal processing techniques and applies it to the vibration reduction of hard disk drives (HDD). Specifically, this work focuses on the implementation of the adaptive algorithm in the frequency and sub-band domains for performance improvement.In this dissertation, selective adaptation in the frequency domain is proposed to alleviate the constructive interference associated with a feedback active control system. A new sub-band adaptive filter architecture without a signal path delay is proposed, and the associated adaptive algorithm is derived. This delayless sub-band algorithm can be applied to the active control systems to improve the convergence rate and trade-off the performance from the various sub-bands. The resulting side effect of the error path delay of the analysis filter bank is analyzed, and two compensation methods are proposed to increase the performance. The frequency domain method and the sub-band decomposition technique are then combined to improve the overall performance. The single-channel active control system is extended to the multiple-channel active control system to reduce the vibration of complex mechanical structure. The optimal performances of three variants of the feedback control system have been derived in terms of the correlation coefficients of the primary disturbances and the impulse responses of the secondary paths. Real time and simulation results are performed to verify the efficiency of the proposed algorithms and techniques

On Improvement of Proportionate Adaptive Algorithms for Sparse Impulse Response

高知工科大学博士(工学) 平成21年9月30日授与 (甲第168号