An International Journal, A publication of the European Association for Signal Processing (EURASIP)

Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work covering novel signal processing tools as well as tutorial and review articles with a focus on the signal processing issues. It is intended for a rapid dissemination of knowledge to engineers and scientists working in the research, development or practical application of signal processing.

Subject areas covered by the journal include:

    Statistical Signal Processing;
    Detection and Estimation;
    Spectral Analysis and Filtering;
    Machine Learning for Signal Processing;
    Optimization methods for Signal Processing;
    Multi-dimensional Signal Processing;
    Graph Signal Processing;
    Signal Processing over Networks;
    Signal Processing for Communications and networking;
    Biomedical Signal Processing;
    Image and Video Processing;
    Audio and Acoustic Signal Processing;
    Multimedia Signal Processing;
    Radar and Sonar Signal Processing;
    Remote Sensing;
    Data Science;
    Network Science;
    Software Developments and Open Source Initiatives;
    New Applications.

Type of Contributions:
The journal welcomes the following types of contributions.

Original research articles:
Research articles should not exceed 30 pages (single column, double spaced, including figures, tables and references) in length and must contain novel research within the scope of the journal.

Review articles:
Review articles are typically 30-60 pages (single column, double spaced, including figures tables and references) in length, and provide a comprehensive review on a scientific topic. They may be relatively broad in scope, thereby serving a tutorial function, or be quite specialized, aimed at researchers in the chosen field.

Fast Communications:
A Fast Communication is a short, self-contained article highlighting ongoing research, or reporting interesting possibly tentative ideas, or comments on previously published research. Such articles should not exceed 10 pages (single column, double spaced, including figures, tables and references) in length. The objective is to provide detailed, constructive feedback on submitted papers and publish high quality papers within a very short period of time.

Special Issue on Signal Processing and Learning with Manifolds and Lie Groups
Submission Date: 2026-01-15

The data deluge of our digital era presents profound challenges for data-driven signal processing methods and algorithms. At the core of efforts to extract meaning from massive, high-dimensional datasets lies a longstanding pursuit: uncovering low-dimensional patterns and geometric structures amid the surrounding complexity. A key approach to this challenge is the manifold-modeling assumption, which posits that data---or their salient features---lie on or near low-dimensional, smooth manifolds, which may be known or unknown and are embedded in high-dimensional ambient spaces. This perspective provides a principled and mathematically rigorous framework for identifying and leveraging hidden geometric patterns within data or feature complexity, enabling dimensionality reduction and often leading to significant performance gains in modern signal-processing techniques. Moreover, the mathematical foundations of manifold-based models---spanning differential geometry, Lie-group theory, and Riemannian optimization---are both powerful and versatile. They support a rich landscape of research directions and foster the development of novel methodologies with wide-ranging applicability.

To underscore the importance of manifold-based approaches in addressing contemporary data-driven signal processing and learning challenges, this special issue aims to showcase cutting-edge techniques for learning with manifolds. Given the inherently interdisciplinary nature of this field, we welcome contributions from researchers and practitioners in engineering, computer science, and mathematics---particularly those with a focus on signal-processing applications.

Guest editors:

Dr. Audrey Giremus
University of Bordeaux, France

Dr. Konstantinos Slavakis
Institute of Science Tokyo, Japan

Dr. Wee Peng Tay
Nanyang Technological University, Singapore

Special issue information:

A tentative list of topics includes but is not limited to the following: manifold learning, optimization on manifolds, low-rank modeling with manifolds, graph signal processing with manifolds, deep learning and graph neural networks with manifolds, dimensionality reduction, online learning with manifolds, learning with tensor manifolds, learning with Lie groups, filtering on Lie groups.

Manuscript submission information:

    Manuscript submission deadline: 15/01/2026
    Submission Site: Editorial Manager®
    Article Type Name: "VSI: SIGPRO_Manifolds and Lie Groups" - please select this item when you submit manuscripts online

All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue.
For any inquiries about the appropriateness of contribution topics, welcome to contact Leading Guest Editor Dr. Audrey Giremus via audrey.giremus@u-bordeaux.fr.
Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Signal Processing - ISSN 0165-1684 | ScienceDirect.com by Elsevier
For more information about our Journal, please visit our ScienceDirect Page: Signal Processing | Journal | ScienceDirect.com by Elsevier

Keywords:

Manifold, information geometry, Lie groups, learning, graph signal processing

Special Issue on Clustering for Signal Processing: Challenges, Advances, and Emerging Applications
Submission Date: 2026-08-31

Clustering serves as a fundamental technique in signal processing and unsupervised learning, playing a crucial role in uncovering hidden structures and patterns within complex signals. Its broad applicability has made it an indispensable tool in diverse domains, including speech and audio processing, biomedical signal analysis, remote sensing, and wireless communications. From separating audio sources in speech enhancement to classifying hyperspectral remote sensing images, clustering methods have significantly impacted both academic research and real-world signal processing applications.

However, as signal data continues to grow in volume, complexity, and diversity, traditional clustering methods face substantial challenges. High-dimensional signal representations, time-varying environments, and multi-modal data sources demand algorithms that are adaptive, scalable, and capable of delivering robust performance under uncertain conditions. Moreover, the interpretability of clustering results has become increasingly critical, particularly in applications such as brain signal analysis, healthcare diagnostics, and cybersecurity.

Recent advancements in clustering methodologies have sought to address these challenges by leveraging deep learning, graph signal processing, self-supervised learning, and optimization-driven frameworks. Meanwhile, the applications of clustering in signal processing have expanded into emerging areas, including IoT-based sensor networks, multi-channel biomedical monitoring, and intelligent communication systems.

This special issue aims to bring together cutting-edge research on clustering techniques specifically tailored for signal processing, with a focus on theoretical advancements, novel methodologies, and practical applications. We invite high-quality submissions that address the challenges of clustering in complex signal environments, particularly in dynamic, multi-view, high-dimensional, and large-scale signal data settings. By fostering a platform for sharing innovative approaches and interdisciplinary applications, this special issue seeks to advance clustering as a crucial tool in modern signal processing, artificial intelligence, and data science.

Topics of interest for this special issue include, but are not limited to:

    Theoretical advancements in clustering methodologies for signal processing
    Clustering techniques for high-dimensional and large-scale signal data
    Dynamic and online clustering for time-series and streaming signals
    Multi-modal and multi-view clustering for heterogeneous signal sources
    Robust clustering for noisy and corrupted signal data
    Graph-theoretical clustering methods in signal representation and analysis
    Interpretable and explainable clustering techniques in signal processing applications

Guest editors:

Prof. Badong Chen
Xi'an Jiaotong University, Xi'an, China

Dr. Ben Yang
Xi'an Jiaotong University, Xi'an, China

Dr. Lei Xing
Xi'an Jiaotong University, Xi'an, China

Dr. Jose Principe
University of Florida, Gainesville, United States

Manuscript submission information:

    Open for Submission: from 31-Aug-2025 to 31-Aug-2026
    Submission Site: Editorial Manager®
    Article Type Name: "VSI: SIGPRO_Clustering for Signal Processing" - please select this item when you submit manuscripts online

All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue.
For any inquiries about the appropriateness of contribution topics, welcome to contact Leading Guest Editor (Prof. Badong Chen).
Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Signal Processing - ISSN 0165-1684 | ScienceDirect.com by Elsevier
For more information about our Journal, please visit our ScienceDirect Page: Signal Processing | Journal | ScienceDirect.com by Elsevier

Keywords:

    Signal Clustering; 
    High-dimensional Data; 
    Online Clustering; 
    Multi-view Learning; 
    Robust Clustering; 
    Graph Signals; 
    Biomedical Signals; 
    IoT & Communications