IEEE Transactions on Signal Processing

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Scope

The IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals. The term “signal” includes, among others, audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. Examples of topics of interest include, but are not limited to, information processing and the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.

The scope is reflected in the EDICS: the Editors` Information Classification Scheme.
The Transactions publish original, timely and significant contributions. Submissions must be previously unpublished and may not be under considerations elsewhere. Technical papers are submitted via Manuscript Central (see Author`s Instructions). Please consider the journal with the most appropriate scope for your submission.

Abstracts and indexing

The Transactions is listed in AMS MathSciNet (Mathematical Reviews Database), Current Contents (Engineering, Computing & Technology, Electronics & Telecommunications Collection), CompuMath citation index, EI Compendex, IEE INSPEC, ISI Science Citation Index, ISI SciSearch, Scitation Research Alerts, PubMed, Medline.

Reproducible research

The Transactions encourages authors to make their publications reproducible by making all information needed to reproduce the presented results available online. This typically requires publishing the code and data used to produce the publication`s figures and tables on a website; see the supplemental materials section of the information for authors. It gives other researchers easier access to the work, and facilitates fair comparisons.

Multimedia content

It is now possible to submit for review and publish in Xplore supporting multimedia material such as speech samples, images, movies, matlab code etc. A multimedia graphical abstract can also be displayed along with the traditional text. More information is available under Multimedia Materials at the IEEE Author Center.

TSP Volume 72 | 2024

Leveraging Variational Autoencoders for Parameterized MMSE Estimation

TSP Volume 72 | 2024
TSP Articles

In this manuscript, we propose to use a variational autoencoder-based framework for parameterizing a conditional linear minimum mean squared error estimator. The variational autoencoder models the underlying unknown data distribution as conditionally Gaussian, yielding the conditional first and second moments of the estimand, given a noisy observation.

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Learning Graph ARMA Processes From Time-Vertex Spectra

TSP Volume 72 | 2024
TSP Articles

The modeling of time-varying graph signals as stationary time-vertex stochastic processes permits the inference of missing signal values by efficiently employing the correlation patterns of the process across different graph nodes and time instants. In this study, we propose an algorithm for computing graph autoregressive moving average (graph ARMA) processes based on learning the joint time-vertex power spectral density of the process from its incomplete realizations for the task of signal interpolation. 

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Coordinating Multiple Intelligent Reflecting Surfaces Without Channel Information

TSP Volume 72 | 2024
TSP Articles

Conventional beamforming methods for intelligent reflecting surfaces (IRSs) or reconfigurable intelligent surfaces (RISs) typically entail the full channel state information (CSI). However, the computational cost of channel acquisition soars exponentially with the number of IRSs. To bypass this difficulty, we propose a novel strategy called blind beamforming that coordinates multiple IRSs by means of statistics without knowing CSI.

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Neural Enhanced Belief Propagation for Multiobject Tracking

TSP Volume 72 | 2024
TSP Articles

Algorithmic solutions for multi-object tracking (MOT) are a key enabler for applications in autonomous navigation and applied ocean sciences. State-of-the-art MOT methods fully rely on a statistical model and typically use preprocessed sensor data as measurements. In particular, measurements are produced by a detector that extracts potential object locations from the raw sensor data collected at discrete time steps. This preparatory processing step reduces data flow and computational complexity but may result in a loss of information. 

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Ensembled Seizure Detection Based on Small Training Samples

TSP Volume 72 | 2024
TSP Articles

This paper proposes an interpretable ensembled seizure detection procedure using electroencephalography (EEG) data, which integrates data driven features and clinical knowledge while being robust against artifacts interference.

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