7,038 research outputs found
An affine combination of two LMS adaptive filters - Transient mean-square analysis
This paper studies the statistical behavior of an affine combination of the outputs of two LMS adaptive filters that simultaneously adapt using the same white Gaussian inputs. The purpose of the combination is to obtain an LMS adaptive filter with fast convergence and small steady-state mean-square deviation (MSD). The linear combination studied is a generalization of the convex combination, in which the combination factor is restricted to the interval . The viewpoint is taken that each of the two filters produces dependent estimates of the unknown channel. Thus, there exists a sequence of optimal affine combining coefficients which minimizes the MSE. First, the optimal unrealizable affine combiner is studied and provides the best possible performance for this class. Then two new schemes are proposed for practical applications. The mean-square performances are analyzed and validated by Monte Carlo simulations. With proper design, the two practical schemes yield an overall MSD that is usually less than the MSD's of either filter
Plant identification via adaptive combination of transversal filters
For least mean-square (LMS) algorithm applications, it is important to improve the speed of convergence vs the residual error trade-off imposed by the selection of a certain value for the step size. In this paper, we propose to use a mixture approach, adaptively combining two independent LMS filters with large and small step sizes to obtain fast convergence with low misadjustment during stationary periods. Some plant identification simulation examples show the effectiveness of our method when compared to previous variable step size approaches. This combination approach can be straightforwardly extended to other kinds of filters, as it is illustrated with a convex combination of recursive least-squares (RLS) filters.Publicad
Adaptive Algorithms for Intelligent Acoustic Interfaces
Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the “last-mile” problem of telecommunications.
One of the main feature of immersive communications is the distant-talking,
i.e. the hands-free (in the broad sense) speech communications without bodyworn
or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms.
The acoustic path modelling entails a set of problems which have to be taken into account in designing an adaptive filtering algorithm. Such problems may be basically generated by a linear or a nonlinear process and can be tackled respectively by linear or nonlinear adaptive algorithms.
In this work we consider such modelling problems and we propose novel effective adaptive algorithms that allow acoustic interfaces to be robust against any interfering signals, thus preserving the perceived quality of desired speech signals.
As regards linear adaptive algorithms, a class of adaptive filters based on the
sparse nature of the acoustic impulse response has been recently proposed.
We adopt such class of adaptive filters, named proportionate adaptive filters, and derive a general framework from which it is possible to derive any linear adaptive algorithm. Using such framework we also propose some efficient proportionate adaptive algorithms, expressly designed to tackle problems of a linear nature.
On the other side, in order to address problems deriving from a nonlinear process, we propose a novel filtering model which performs a nonlinear transformations by means of functional links. Using such nonlinear model, we propose functional link adaptive filters which provide an efficient solution to the modelling of a nonlinear acoustic channel.
Finally, we introduce robust filtering architectures based on adaptive combinations of filters that allow acoustic interfaces to more effectively adapt to environment conditions, thus providing a powerful mean to immersive speech communications
Adaptive Algorithms for Intelligent Acoustic Interfaces
Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the “last-mile” problem of telecommunications.
One of the main feature of immersive communications is the distant-talking,
i.e. the hands-free (in the broad sense) speech communications without bodyworn
or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms.
The acoustic path modelling entails a set of problems which have to be taken into account in designing an adaptive filtering algorithm. Such problems may be basically generated by a linear or a nonlinear process and can be tackled respectively by linear or nonlinear adaptive algorithms.
In this work we consider such modelling problems and we propose novel effective adaptive algorithms that allow acoustic interfaces to be robust against any interfering signals, thus preserving the perceived quality of desired speech signals.
As regards linear adaptive algorithms, a class of adaptive filters based on the
sparse nature of the acoustic impulse response has been recently proposed.
We adopt such class of adaptive filters, named proportionate adaptive filters, and derive a general framework from which it is possible to derive any linear adaptive algorithm. Using such framework we also propose some efficient proportionate adaptive algorithms, expressly designed to tackle problems of a linear nature.
On the other side, in order to address problems deriving from a nonlinear process, we propose a novel filtering model which performs a nonlinear transformations by means of functional links. Using such nonlinear model, we propose functional link adaptive filters which provide an efficient solution to the modelling of a nonlinear acoustic channel.
Finally, we introduce robust filtering architectures based on adaptive combinations of filters that allow acoustic interfaces to more effectively adapt to environment conditions, thus providing a powerful mean to immersive speech communications
Steady-State Performance of an Adaptive Combined MISO Filter Using the Multichannel Affine Projection Algorithm
The combination of adaptive filters is an effective approach to improve filtering performance. In this paper, we investigate the performance of an adaptive combined scheme between two adaptive multiple-input single-output (MISO) filters, which can be easily extended to the case of
multiple outputs. In order to generalize the analysis, we consider the multichannel affine projection
algorithm (APA) to update the coefficients of the MISO filters, which increases the possibility of
exploiting the capabilities of the filtering scheme. Using energy conservation relations, we derive
a theoretical behavior of the proposed adaptive combination scheme at steady state. Such analysis
entails some further theoretical insights with respect to the single channel combination scheme.
Simulation results prove both the validity of the theoretical steady-state analysis and the effectiveness
of the proposed combined scheme.The work of Danilo Comminiello, Michele Scarpiniti and Aurelio Uncini has been supported by the
project: “Vehicular Fog energy-efficient QoS mining and dissemination of multimedia Big Data streams (V-FoG
and V-Fog2)”, funded by Sapienza University of Rome Bando 2016 and 2017. The work of Michele Scarpiniti
and Aurelio Uncini has been also supported by the project: “GAUChO – A Green Adaptive Fog Computing and
networking Architectures” funded by the MIUR Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN) Bando
2015, grant 2015YPXH4W_004. The work of Luis A. Azpicueta-Ruiz is partially supported by the Spanish Ministry
of Economy and Competitiveness (under grant DAMA (TIN2015-70308-REDT) and grants TEC2014-52289-R and
TEC2017-83838-R), and by the European Union
Sparse Distributed Learning Based on Diffusion Adaptation
This article proposes diffusion LMS strategies for distributed estimation
over adaptive networks that are able to exploit sparsity in the underlying
system model. The approach relies on convex regularization, common in
compressive sensing, to enhance the detection of sparsity via a diffusive
process over the network. The resulting algorithms endow networks with learning
abilities and allow them to learn the sparse structure from the incoming data
in real-time, and also to track variations in the sparsity of the model. We
provide convergence and mean-square performance analysis of the proposed method
and show under what conditions it outperforms the unregularized diffusion
version. We also show how to adaptively select the regularization parameter.
Simulation results illustrate the advantage of the proposed filters for sparse
data recovery.Comment: to appear in IEEE Trans. on Signal Processing, 201
Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies
The goal of this paper is to propose novel strategies for adaptive learning
of signals defined over graphs, which are observed over a (randomly
time-varying) subset of vertices. We recast two classical adaptive algorithms
in the graph signal processing framework, namely, the least mean squares (LMS)
and the recursive least squares (RLS) adaptive estimation strategies. For both
methods, a detailed mean-square analysis illustrates the effect of random
sampling on the adaptive reconstruction capability and the steady-state
performance. Then, several probabilistic sampling strategies are proposed to
design the sampling probability at each node in the graph, with the aim of
optimizing the tradeoff between steady-state performance, graph sampling rate,
and convergence rate of the adaptive algorithms. Finally, a distributed RLS
strategy is derived and is shown to be convergent to its centralized
counterpart. Numerical simulations carried out over both synthetic and real
data illustrate the good performance of the proposed sampling and
reconstruction strategies for (possibly distributed) adaptive learning of
signals defined over graphs.Comment: Submitted to IEEE Transactions on Signal Processing, September 201
Collaborative adaptive filtering for machine learning
Quantitative performance criteria for the analysis of machine learning architectures
and algorithms have long been established. However, qualitative performance criteria,
which identify fundamental signal properties and ensure any processing preserves the
desired properties, are still emerging. In many cases, whilst offline statistical tests
exist such as assessment of nonlinearity or stochasticity, online tests which not only
characterise but also track changes in the nature of the signal are lacking. To that end,
by employing recent developments in signal characterisation, criteria are derived for
the assessment of the changes in the nature of the processed signal.
Through the fusion of the outputs of adaptive filters a single collaborative hybrid
filter is produced. By tracking the dynamics of the mixing parameter of this filter,
rather than the actual filter performance, a clear indication as to the current nature of
the signal is given. Implementations of the proposed method show that it is possible to
quantify the degree of nonlinearity within both real- and complex-valued data. This is
then extended (in the real domain) from dealing with nonlinearity in general, to a more
specific example, namely sparsity. Extensions of adaptive filters from the real to the
complex domain are non-trivial and the differences between the statistics in the real
and complex domains need to be taken into account. In terms of signal characteristics,
nonlinearity can be both split- and fully-complex and complex-valued data can be
considered circular or noncircular. Furthermore, by combining the information obtained
from hybrid filters of different natures it is possible to use this method to gain a more
complete understanding of the nature of the nonlinearity within a signal. This also
paves the way for building multidimensional feature spaces and their application in
data/information fusion.
To produce online tests for sparsity, adaptive filters for sparse environments are
investigated and a unifying framework for the derivation of proportionate normalised
least mean square (PNLMS) algorithms is presented. This is then extended to derive
variants with an adaptive step-size. In order to create an online test for noncircularity,
a study of widely linear autoregressive modelling is presented, from which a proof of
the convergence of the test for noncircularity can be given. Applications of this method
are illustrated on examples such as biomedical signals, speech and wind data
Information Acquisition with Sensing Robots: Algorithms and Error Bounds
Utilizing the capabilities of configurable sensing systems requires
addressing difficult information gathering problems. Near-optimal approaches
exist for sensing systems without internal states. However, when it comes to
optimizing the trajectories of mobile sensors the solutions are often greedy
and rarely provide performance guarantees. Notably, under linear Gaussian
assumptions, the problem becomes deterministic and can be solved off-line.
Approaches based on submodularity have been applied by ignoring the sensor
dynamics and greedily selecting informative locations in the environment. This
paper presents a non-greedy algorithm with suboptimality guarantees, which does
not rely on submodularity and takes the sensor dynamics into account. Our
method performs provably better than the widely used greedy one. Coupled with
linearization and model predictive control, it can be used to generate adaptive
policies for mobile sensors with non-linear sensing models. Applications in gas
concentration mapping and target tracking are presented.Comment: 9 pages (two-column); 2 figures; Manuscript submitted to the 2014
IEEE International Conference on Robotics and Automatio
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