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 Clustering for Signal Processing: Challenges, Advances, and Emerging Applications
截稿日期: 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

Special Issue on Beneficial Noise Processing in Multimodal Signal Processing
截稿日期: 2026-12-15

Beneficial noise processing is a new keyword and an emerging field in signal processing (especially times series) and artificial intelligence. In conventional signal processing, noise removal is a classical problem. However, as shown by stochastic resonance, the noise can be beneficial if the noise is at an appropriate level. In recent years, the noise-based models have attracted more and more attention, including but not limited to random forest, dropout in neural networks (a kind of structural beneficial noise), generative adversarial networks, adversarial training, noisy augmentation, diffusion models, and flow matching models. In particular, with the rise of multimodal large models, more and more researchers try applying multimodal large models to signal processing tasks. In particular, the augmentation and generation of time series is highly related to beneficial noise learning. Although most of these models don’t explicitly claim that they aim to learn noise, they actually utilize the beneficial noise implicitly. In many current studies, it is pointed out that noise can be also beneficial to multimodal models. Noise should not be regarded as a harmful component any more. The benefits of noise deserve more systematic studies. However, scientific studies of beneficial noise learning, especially in multimodal signal processing, are still lacking to some extent. Most of these noise-based models just use beneficial noise in a heuristic way.

This Special Issue seeks to cover a wide range of topics related to beneficial noise learning and analysis, including but not limited to:

1. Noise-based multimodal generative models for signals;
2. Beneficial noisy and uncertain structure in multimodal models for signal processing;
3. Noisy model training multimodal signal;
4. Noisy augmentations for signal;
5. Positive-incentive noise;
6. Explainable analysis for beneficial noise in multimodal signal processing.

Guest editors:

Dr. Hongyuan Zhang
The University of Hong Kong, Hong Kong, China
hyzh98@hku.hk

Prof. Xuelong Li
Institute of Artificial Intelligence (TeleAI), China Telecom, Beijing, China
xuelongli.iopen@gmail.com

Prof. Feiping Nie
Northwestern Polytechnical University, Xi’an, China
feipingnie@gmail.com

Manuscript submission information:

Submission Open Date: 01/01/2026
Manuscript submission deadline: 15/12/2026
Submission Site: Editorial Manager®
Article Type Name: "VSI: Benef. Noise 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 Dr. Hongyuan Zhang via hyzh98@hku.hk.
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:

Beneficial Noise Processing, Noise Learning, Multimodal Signal Processing

https://www.sciencedirect.com/special-issue/329018/beneficial-noise-processing-in-multimodal-signal-processing