Luis Antonio Azpicueta-Ruiz | Universidad Carlos III de Madrid (original) (raw)

Papers by Luis Antonio Azpicueta-Ruiz

El tribunal nombrado para juzgar la tesis doctoral arriba citada, compuesto por los doctores Pres... more El tribunal nombrado para juzgar la tesis doctoral arriba citada, compuesto por los doctores Presidente:

Edición sostenible editado como documento electrónico de lectura en pantalla. Si no es necesario,... more Edición sostenible editado como documento electrónico de lectura en pantalla. Si no es necesario, no los imprimas. Si los imprimes, hazlo a doble cara.

2009 Ieee International Conference on Acoustics, Speech, and Signal Processing, Vols 1- 8, Proceedings, 2009

This paper presents a method for estimating the optimum memory size for identification of an unkn... 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 filters with time-variant sizes of the actually used quadratic kernels. By following some simple rules, the number of diagonals in the quadratic kernels is increased or decreased in order to find the optimum memory configuration in parallel to the coefficient adaptation. Thus, the arbitrary choice of the nonlinear system size is overcome by a dynamically growing/shrinking system. Experimental results for various signals and nonlinear scenarios demonstrate the effectiveness of the proposed method.

2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2009

This paper presents a novel strategy of adaptive filtering which provides an automatic self-confi... more This paper presents a novel strategy of adaptive filtering which provides an automatic self-configuration of the filter structure in terms of memory length. By monitoring the adaptive mixing of a normalized combination of two competing filters with a different number of coefficients, an online estimate of the optimum filter length is obtained and used to dynamically scale the size of the employed filters. Furthermore, a more efficient, simplified version of this approach is proposed and shown to be equally effective while significantly reducing the required complexity. Experimental results for high-order real-world systems as well as stationary noise and speech signals demonstrate the good performance and the robust tracking behaviour of the outlined algorithms in the context of realistic system identification scenarios.

Digital Signal Processing, 2015

ABSTRACT We propose a diffusion scheme for adaptive networks, where each node obtains an estimate... more ABSTRACT We propose a diffusion scheme for adaptive networks, where each node obtains an estimate of a common unknown parameter vector by combining a local estimate with the combined estimates received from neighboring nodes. The combination weights are adapted in order to minimize the mean-square error of the network employing a local least-squares (LS) cost function. This adaptive diffusion network with LS combiners (ADN-LS) is analyzed, deriving expressions for its network mean-square deviation that characterize the convergence and steady-state performance of the algorithm. Experiments carried out in stationary and tracking scenarios show that our proposal outperforms an state-of-art scheme for adapting the weights of diffusion networks (ACW algorithm from [10]), both during convergence and in tracking situations. Despite its good convergence behavior, our proposal may present a slightly worse steady-state performance in stationary or slowly-changing scenarios with respect to ACW due to the error inherent to the least-squares adaptation with sliding window. Therefore, to take advantage of these different behaviors, we also propose a hybrid scheme based on a convex combination of the ADN-LS and ACW algorithms.

2011 Joint Workshop on Hands-free Speech Communication and Microphone Arrays, 2011

This paper introduces a collaborative architecture for nonlinear acoustic echo cancellation. This... more This paper introduces a collaborative architecture for nonlinear acoustic echo cancellation. This system is achieved linearly combining a standard linear filter and a functional link (FL) nonlinear filter. The FL filter is composed of merely nonlinear elements thus acting like a pure nonlinear kernel and modeling at best a nonlinear path. A more robust architecture is further introduced in which

Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 2010

Adaptive combinations of adaptive filters are gaining popularity as a flexible and versatile solu... more Adaptive combinations of adaptive filters are gaining popularity as a flexible and versatile solution to improve adaptive filters performance. In the recent years, combination schemes have focused on two different approaches: Convex and affine combinations, developing principally practical implementations with just two component filters. However, combinations of a higher number of adaptive filters can offer additional advantages, mainly in tracking environments. In this paper, we introduce a practical adaptation scheme for the affine combination of an arbitrary number of filters, including a steady-state analysis where the proposed rule is compared with the optimal combination. Several experiments both in tracking and stationary scenarios serve to demonstrate the appropriate performance of this approach.

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010

This paper presents a method for estimating the optimum number of second-order kernel diagonals o... more This paper presents a method for estimating the optimum number of second-order kernel diagonals of an adaptive Volterra filter in system identification tasks. To this end, a recently proposed timedomain mechanism is carried over to the very efficient partitionedblock DFT-domain Volterra filtering technique. The size of the nonlinear memory is controlled by monitoring the performance of an adaptive combination scheme with two differently-sized quadratic kernels. Subsequently, an efficient version is derived, requiring only minor additional computations as compared to a single Volterra filter. The effectiveness of the outlined estimation procedure is demonstrated by various simulations with real nonlinear systems and both noise and speech inputs in an acoustic echo cancellation scenario.

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009

Nonlinear acoustic echo cancellers (NLAEC) are becoming increasingly important in hands-free appl... 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 time, thus making it difficult to know if an NLAEC would improve or degrade the cancellation. In this paper, we present two novel solutions to this problem based on the adaptive combination of linear and nonlinear echo cancellers. Both solutions perform efficiently regardless of the level of nonlinear echo. The benefits and robustness of both schemes are illustrated by experiments using Laplacian colored noise and speech input signals.

2008 IEEE Workshop on Machine Learning for Signal Processing, 2008

Adaptive combinations of adaptive filters are an efficient approach to alleviate the different tr... more Adaptive combinations of adaptive filters are an efficient approach to alleviate the different tradeoffs to which adaptive filters are subject. The basic idea is to mix the outputs of two adaptive filters with complementary capabilities, 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 λ(n) and 1 − λ(n), with λ(n) ∈ (0, 1), to the filter components, where the mixing parameter λ(n) was updated to minimize the overall square error using stochastic gradient descent rules. In this paper, we present a new adaptation scheme for λ(n) based on the solution to a least-squares (LS) 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 LS combination scheme does not require any explicit knowledge about the component filters. The ability of the LS scheme to achieve optimal values of the mixing parameter is illustrated with several experiments in both stationary and tracking situations.

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers, 2009

ABSTRACT Combination schemes are gaining attention as an interesting way to improve adaptive filt... more ABSTRACT Combination schemes are gaining attention as an interesting way to improve adaptive filter performance. In this paper, we present some recently proposed practical schemes for convex and affine filter combination, explaining how they can be useful to overcome some of the most important limitations of conventional adaptive filters. We will also illustrate the benefits of this approach with several experiments. First, we explain how the combination can reduce the mean square error (MSE) of adaptive filters by biasing their outputs. We present also some recent results in real scenarios for acoustic echo cancellation.

2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011

Abstract Adaptive filters are crucial in many signal processing applications. Recently, a simple ... more Abstract Adaptive filters are crucial in many signal processing applications. Recently, a simple configuration was presented to introduce a bias in the estimation of adaptive filters using a multiplicative factor, showing important gains in terms of mean square error with ...

2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008

Adaptive filtering schemes are subject to different tradeoffs regarding their steady-state misadj... more 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 performance. Following this approach, in this paper we propose a new normalized rule for adapting the mixing parameter that controls the combination. The new update rule preserves the good features of the standard scheme and is more robust to changes in the filtering scenario, for instance when the signal to noise ratio (SNR) is time varying. The benefits of the normalized scheme are illustrated analytically and with a number of experiments in both stationary and tracking situations.

IEEE Transactions on Signal Processing, 2000

It is a well-known result of estimation theory that biased estimators can outperform unbiased one... 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 adaptively biasing the weights of adaptive filters using an output multiplicative factor. We give theoretical results that show that the proposed configuration is able to provide a convenient bias vs variance tradeoff, leading to reductions in the filter mean-square error, especially in situations with a low signal-to-noise ratio (SNR). After reinterpreting the biased estimator as the combination of the original filter and a filter with constant output equal to 0, we propose practical schemes to adaptively adjust the multiplicative factor. Experiments are carried out for the normalized leastmean-squares (NLMS) adaptive filter, improving its mean-square performance in stationary situations and during the convergence phase.

IEEE Transactions on Signal Processing, 2000

This paper proposes a new paradigm for adaptive Volterra filtering using a time-variant size of t... 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 of the second-order kernel memory, using the already known diagonal-coordinate representation. The length and number of required quadratic kernel diagonals can be concurrently estimated by monitoring the combination performance. Subsequently, the memory size of the involved models is dynamically increased or decreased, following a set of intuitive rules. Since this automatic memory adaptation is performed along with the coefficient updates, an efficient Volterra filter is realized, offering great flexibility and minimizing the risk of under-or overmodeling any given quadratic nonlinearity. Besides the straightforward scheme, a simplified version is presented, greatly reducing the algorithmic demands. This efficient version is based on a virtualization of the competing Volterra filters by jointly using common coefficients and hence exhibits a computational complexity suitable for practical implementations. The robust estimation performance of the approach is demonstrated Manuscript

IEEE Transactions on Audio, Speech, and Language Processing, 2000

This study presents a preliminary investigation into the automatic assessment of Language Impaire... more This study presents a preliminary investigation into the automatic assessment of Language Impaired Children's (LIC) prosodic skills in one grammatical aspect: sentence modalities. Three types of language impairments were studied: Autism Disorder (AD), Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS) and Specific Language Impairment (SLI). A control group of Typically Developing (TD) children that was both age and gender matched with LIC was used for the analysis. All of the children were asked to imitate sentences that provided different types of intonation (e.g., descending and rising contours). An automatic system was then used to assess LIC's prosodic skills by comparing the intonation recognition scores with those obtained by the control group. The results showed that all LIC have difficulties in reproducing intonation contours because they achieved significantly lower recognition scores than TD children on almost all studied intonations (p<0.05). Regarding the "Rising" intonation, only SLI children had high recognition scores similar to TD children, which suggests a more pronounced pragmatic impairment in AD and PDD-NOS children. The automatic approach used in this study to assess LIC's prosodic skills confirms the clinical descriptions of the subjects' communication impairments.

In this paper we analyze a novel diffusion strategy for adaptive networks called Decoupled Adapt-... more In this paper we analyze a novel diffusion strategy for adaptive networks called Decoupled Adapt-then-Combine, which keeps a fully local estimate of the solution for the adaptation step. Our strategy, which is specially convenient for heterogeneous networks, is compared with the standard Adapt-then-Combine scheme and theoretically analyzed using energy conservation arguments. Such comparison shows the need of implementing adaptive combiners for both schemes to obtain a good performance in case of heterogeneous networks. Therefore, we propose two adaptive rules to learn the combination coefficients that are useful for our diffusion strategy. Several experiments simulating both stationary estimation and tracking problems show that our method outperforms state-of-the-art techniques, becoming a competitive approach in different scenarios.

ABSTRACT Adaptive networks have received great attention during recent years. In diffusion strate... more ABSTRACT Adaptive networks have received great attention during recent years. In diffusion strategies, nodes diffuse their estimations to neighbors, and construct improved estimates by combining all information received by other nodes. When nodes work in heterogeneous conditions, it is reasonable to assign combination weights that take into account the performance of each node; thus, different schemes that implement adaptive combiners have been recently proposed. In this paper, we propose a novel scheme for adaptive combiners which attempts to minimize a least-squares cost function. The novelty in our proposal relies on making the adaptive combiners convex, by projection onto the standard simplex, what result in a numerically more stable implementation. The convergence and steady-state properties of the new scheme are analyzed theoretically, and its performance is experimentally evaluated with respect to existing methods.

2012 IEEE International Workshop on Machine Learning for Signal Processing, 2012

ABSTRACT In this paper, we propose a novel diffusion scheme for adaptive networks, where each nod... more ABSTRACT In this paper, we propose a novel diffusion scheme for adaptive networks, where each node preserves a pure local estimate of the unknown parameter vector and combines this estimate with other estimates received from neighboring nodes. The combination weights are adapted to minimize a local least-squares cost function. Simulations carried out in stationary and nonstationary scenarios show that the proposed scheme can outperform other existing schemes for diffusion networks with adaptive combiners in terms of tracking capability and convergence rate when the network nodes use different step sizes.