Journal Information

Signal Processing (SIGPRO)

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Impact Factor:
3.6
Publisher:
Elsevier
ISSN:
0165-1684
Viewed:
31613
Tracked:
23

Call For Papers

Signal Processing (SIGPRO) is an academic journal published by Elsevier. (ISSN 0165-1684, impact factor 3.6, CCF C).

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.
Last updated by Dou Sun on

Special Issues

Special Issue on Beneficial Noise Processing in Multimodal Signal Processing Submission Date: 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 [email protected] Prof. Xuelong Li Institute of Artificial Intelligence (TeleAI), China Telecom, Beijing, China [email protected] Prof. Feiping Nie Northwestern Polytechnical University, Xi’an, China [email protected] 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 [email protected]. 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
Last updated by Dou Sun on

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