Combined Sparse Regularization for Nonlinear Adaptive Filters

Nonlinear adaptive filters often show some sparse behavior due to the fact that not all the coefficients are equally useful for the modeling of any nonlinearity. Recently, a class of proportionate algorithms has been proposed for nonlinear filters to leverage sparsity of their coefficients. However, the choice of the norm penalty of the cost function may be not always appropriate depending on the problem. In this paper, we introduce an adaptive combined scheme based on a block-based approach involving two nonlinear filters with different regularization that allows to achieve always superior performance than individual rules. The proposed method is assessed in nonlinear system identification problems, showing its effectiveness in taking advantage of the online combined regularization.Comment: This is a corrected version of the paper presented at EUSIPCO 2018 and published on IEEE https://ieeexplore.ieee.org/document/855295

Adaptive diffusion schemes for heterogeneous networks

In this paper, we deal with distributed estimation problems in diffusion networks with heterogeneous nodes, i.e., nodes that either implement different adaptive rules or differ in some other aspect such as the filter structure or length, or step size. Although such heterogeneous networks have been considered from the first works on diffusion networks, obtaining practical and robust schemes to adaptively adjust the combiners in different scenarios is still an open problem. In this paper, we study a diffusion strategy specially designed and suited to heterogeneous networks. Our approach is based on two key ingredients: 1) the adaptation and combination phases are completely decoupled, so that network nodes keep purely local estimations at all times and 2) combiners are adapted to minimize estimates of the network mean-square-error. Our scheme is compared with the standard adapt-Then-combine scheme and theoretically analyzed using energy conservation arguments. Several experiments involving networks with heterogeneous nodes show that the proposed decoupled adapt-Then-combine approach with adaptive combiners outperforms other state-of-The-Art techniques, becoming a competitive approach in these scenarios

Adaptive Filtered-x Algorithms for Room Equalization Based on Block-Based Combination Schemes

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] Room equalization has become essential for sound reproduction systems to provide the listener with the desired acoustical sensation. Recently, adaptive filters have been proposed as an effective tool in the core of these systems. In this context, this paper introduces different novel schemes based on the combination of adaptive filters idea: a versatile and flexible approach that permits obtaining adaptive schemes combining the capabilities of several independent adaptive filters. In this way, we have investigated the advantages of a scheme called combination of block-based adaptive filters which allows a blockwise combination splitting the adaptive filters into nonoverlapping blocks. This idea was previously applied to the plant identification problem, but has to be properly modified to obtain a suitable behavior in the equalization application. Moreover, we propose a scheme with the aim of further improving the equalization performance using the a priori knowledge of the energy distribution of the optimal inverse filter, where the block filters are chosen to fit with the coefficients energy distribution. Furthermore, the biased block-based filter is also introduced as a particular case of the combination scheme, especially suited for low signal-to-noise ratios (SNRs) or sparse scenarios. Although the combined schemes can be employed with any kind of adaptive filter, we employ the filtered-x improved proportionate normalized least mean square algorithm as basis of the proposed algorithms, allowing to introduce a novel combination scheme based on partitioned block schemes where different blocks of the adaptive filter use different parameter settings. Several experiments are included to evaluate the proposed algorithms in terms of convergence speed and steady-state behavior for different degrees of sparseness and SNRs.The work of L. A. Azpicueta-Ruiz was supported in part by the Comtmidad de Madrid through CASI-CAM-CM under Grant S2013/ICE-2845, in part by the Spanish Ministry of Economy and Competitiveness through DAMA under Grant TIN2015-70308-REDT, and Grant TEC2014-52289-R, and in part by the European Union. The work of L. Fuster, M. Ferrer, and M. de Diego was supported in part by EU together with the Spanish Government under Grant TEC2015-67387-C4-1-R (MINECO/FEDER), and in part by the Cieneralitat Valenciana under Grant PROMETEOII/2014/003. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Simon Dodo.Fuster Criado, L.; Diego Antón, MD.; Azpicueta-Ruiz, LA.; Ferrer Contreras, M. (2016). Adaptive Filtered-x Algorithms for Room Equalization Based on Block-Based Combination Schemes. IEEE/ACM Transactions on Audio, Speech and Language Processing. 24(10):1732-1745. https://doi.org/10.1109/TASLP.2016.2583065S17321745241

A split kernel adaptive filtering architecture for nonlinear acoustic echo cancellation

We propose a new linear-in-the-parameters (LIP) nonlinear filter based on kernel methods to address the problem of nonlinear acoustic echo cancellation (NAEC). For this purpose we define a framework based on a parallel scheme in which any kernel-based adaptive filter (KAF) can be incorporated efficiently. This structure is composed of a classic adaptive filter on one branch, committed to estimating the linear part of the echo path, and a kernel adaptive filter on the other branch, to model the nonlinearities rebounding in the echo path. In addition, we propose a novel low-complexity least mean square (LMS) KAF with very few parameters, to be used in the parallel architecture. Finally, we demonstrate the effectiveness of the proposed scheme in real NAEC scenarios, for different choices of the KAF

A new least-squares adaptation scheme for the affine combination of two adaptive filters

Adaptive combinations of adaptive filters are an efficient approach to alleviate the different tradeoffs to which adaptive filters are subject. rrhe basic idea is to mix the outputs of two adaptive filters with complementar~Tcapabilities, so that the combination is able to retain the best properties of each component. In previous works, we proposed to use a convex combination, applying weights.A(n) and 1-.A(n), with A(n) E (0,1), to the filter components, where the mixing parameter.A(n) was updated to minimize the overall square error using stochastic gradient descent rules. In this paper, we present a new adaptation scheme for.A(n) based on the solution to a least-squares (L8) problem, where the mixing parameter is allowed to lie outside range [0, 1]. Such affine combinations have recently been shown to provide additional gains. Unlike some previous proposals, the new L8 cOlnbination scheme does not require any explicit knowledge about the component filters. The ability of the L8 scheme to achieve optimal values ofthe mixing parameter is illustrated with several experiments in both stationary and tracking situations. Index Terms-.Adaptive filters, combination of filters