Luis Antonio Azpicueta-Ruiz

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Adaptive filtering schemes are subject to different tradeoffs regarding their steady-state misadjustment, speed of convergence, and tracking performance. To alleviate these compromises, a new approach has recently been proposed, in which two filters with complementary capabilities adaptively mix their outputs to get an overall filter of improved(More)
The combination of filters concept is a simple and flexible method to circumvent various compromises hampering the operation of adaptive linear filters. Recently, applications which require the identification of not only linear, but also nonlinear systems are widely studied. In this paper, we propose a combination of adaptive Volterra filters as the most(More)
Combinations of adaptive filters have attracted attention as a simple solution to improve filter performance, including tracking properties. In this paper, we consider combinations of LMS and RLS filters, and study their performance for tracking time-varying solutions. We show that a combination of two filters from the same family (i.e., two LMS or two RLS(More)
Proportionate adaptive schemes have been proposed to exploit sparsity and accelerate filter convergence in acoustic echo cancellation. Recently, combinations of adaptive filters have been extended to operate with proportionate schemes, in order to achieve more robust operation when the actual degree of sparsity of the optimal solution is unknown.(More)
This paper introduces a new class of nonlinear adaptive filters, whose structure is based on Hammerstein model. Such filters derive from the functional link adaptive filter (FLAF) model, defined by a nonlinear input expansion, which enhances the representation of the input signal through a projection in a higher dimensional space, and a subsequent adaptive(More)
This paper proposes a new paradigm for adaptive Volterra filtering using a time-variant size of the quadratic kernel memory in order to optimally identify any unknown transversal second-order nonlinear system. To this end, competing Volterra structures of different sizes are employed in a hierarchical combination scheme so as to find the best configuration(More)
Nonlinear acoustic echo cancellers (NLAEC) are becoming increasingly important in hands-free applications. However, in some situations, an NLAEC is inferior to a linear AEC, especially when the channel generates a negligible (or no) nonlinear echo. In general, the ratio of the linear to nonlinear echo signal power is unknown a priori, and will vary over(More)
This paper presents a method for estimating the optimum memory size for identification of an unknown second-order Volterra kernel. As these structures may imply considerable computational demands, it is highly desirable to design adaptive realizations with a minimum number of coefficients. Therefore, we propose a combination scheme comprising two Volterra(More)
It is a well-known result of estimation theory that biased estimators can outperform unbiased ones in terms of expected quadratic error. In steady state, many adaptive filtering algorithms offer an unbiased estimation of both the reference signal and the unknown true parameter vector. In this correspondence, we propose a simple yet effective scheme for(More)
Recently, a new class of nonlinear adaptive filtering architectures has been introduced based on the functional link adaptive filter (FLAF) model. Here we focus specifically on the split FLAF (SFLAF) architecture, which separates the adaptation of linear and nonlinear coefficients using two different adaptive filters in parallel. This property makes the(More)