The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
A multichannel kernel adaptive filtering framework is presented that highlights relevant channels for the task of analyzing Motion Capture (MoCap) data. Functional relevance analysis is performed over input multichannel data by computing the pair-wise channel similarities to describe the main behavior of the considered applications. Particularly, the well-known Kernel Least Mean Square filter is enhanced...
For the nonlinear acoustic echo cancellation, we present an adaptive learning of the saturation effect of the amplifier and the room propagation in terms of the hard-clipping and the FIR system. The conventional learning algorithms are based on a gradient descent method, i.e., rely on local information, which results in a major drawback that the estimation of the hard-clipping is trapped in local...
Acoustic echo cancellation has traditionally employed basically all variants known from deterministic adaptive filter design, such as least mean-square (LMS), recursive least-squares (RLS), and frequency-domain adaptive filters (FDAF). More recently, a stochastic adaptive filter design based on the concept of acoustic state-space modeling of the echo path has been introduced to accommodate for an...
The introduction of data reuse in the incremental topology made it possible for combinations of LMS filters to outperform algorithms such as the Affine Projection Algorithm (APA) with lower complexity. This work poses and extends the concept of combinations as a complexity reduction technique by proposing an incremental combination of sign-error LMS filters that matches and even outperforms stand-alone...
Diffusion strategies for learning across networks which minimize the transient regime mean-square deviation across all nodes are presented. The problem of choosing combination coefficients which minimize the mean-square deviation at all given time instances results in a quadratic program with linear constraints. The implementation of the optimal procedure is based on the estimation of weight deviation...
Beamformer-assisted acoustic echo cancelers have raised a lot of interest lately. The same performance can be obtained with a reduced length acoustic echo canceler (AEC) as the beamformer (BF) performs spatial cancellation. Structures that jointly optimize the BF and the AEC coefficients are preferred in order to exploit synergies. Analytical models have been already derived for the behavior of the...
In this work, we propose an adaptive set-membership (SM) reduced-rank filtering algorithm using the constrained constant modulus (CCM) criterion for beamforming. We develop a stochastic gradient (SG) type algorithm based on the concept of SM technique for adaptive implementation. The filter weights are updated only if the bounded constraint cannot be satisfied. In addition, we also propose a scheme...
An adaptive line enhancer is a self-tuning filter which attempts to retrieve a sinusoid buried in noise. Generally speaking, there is a trade-off between convergence speed and steady-state error. One method to address this is to use a variable step size algorithm, with a large step size for acquisition, and a smaller step size for improved steady-state performance. An alternate topology is based on...
We analyze two algorithms, viz. the affine projection algorithm for sparse system identification (APA-SSI) and the quasi APA-SSI (QAPA-SSI), regarding their stability and steady-state mean-squared error (MSE). These algorithms exploit the sparsity of the involved signals through an approximation of the l0 norm. Such approach yields faster convergence and reduced steady-state MSE, as compared to algorithms...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.