Search results
Enter terms
1174 results found.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
Incorporating graphs in the analysis of multivariate signals is becoming a standard way to understand the interdependency of activity recorded at different sites. The new research frontier in this direction includes the important problem of how to assess dynamic changes of signal activity. We address this problem in a novel way by defining the graph-variate signal alongside methods for its analysis.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
Various signal processing applications can be expressed as large-scale optimization problems with a composite objective structure, where the Lipschitz constant of the smooth part gradient is either not known, or its local values may only be a fraction of the global value. The smooth part may be strongly convex as well. The algorithms capable of addressing this problem class in its entirety are black-box accelerated first-order methods, related to either Nesterov's Fast Gradient Method or the Accelerated Multistep Gradient Scheme, which were developed and analyzed using the estimate sequence mathematical framework.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
Recently, many sparse arrays have been proposed to increase the number of degrees of freedom for direction of arrival (DoA) estimation especially for circular signals. Though many practical signals are noncircular, still the properties of noncircularity are hardly exploited in the design of sparse linear arrays. A new array geometry for noncircular signals, which significantly increases the aperture of the virtual array, has been proposed in this paper.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
Linear regression models contaminated by Gaussian noise (inlier) and possibly unbounded sparse outliers are common in many signal processing applications. Sparse recovery inspired robust regression (SRIRR) techniques are shown to deliver high-quality estimation performance in such regression models. Unfortunately, most SRIRR techniques assume a priori knowledge of noise statistics like inlier noise variance or outlier statistics like number of outliers.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
Distributed estimation fusion is concerned with the combination of local estimates from multiple distributed sensors to produce a fused result. In this paper, we characterize local estimates as posterior probability densities, and assume that they all belong to a parametric family. Our starting point is to consider this family as a Riemannian manifold by introducing the Fisher information metric.
Section: Events
Submission Deadline: February 15, 2019
Call for Proposals Document
Section: Publications & Resources
The IEEE SPS congratulates the following SPS members who will receive the Society’s prestigious awards during ICASSP 2019 in Brighton, UK.
Issue Date:
Section: Community & Involvement
Welcome to the IEEE Speech and Language Processing Technical Committee Newsletter!
Attentive readers will notice that it has been longer than usual since the last edition of the newsletter. I apologize and am totally responsible for the mistake. In penance, we have an extra large edition this time.
Issue Date:
Section: Community & Involvement
Following on the success of the bi-annual SLT workshop over the past decade, the IEEE Speech and Language Technical Committee invites proposals to host the 2020 IEEE Workshop on Spoken Language Technology (SLT 2020).
Issue Date:
Section: Community & Involvement
