IEEE Signal Processing Magazine

Aims & Scope

IEEE Signal Processing Magazine publishes tutorial-style articles on signal processing research and applications, as well as columns and forums on issues of interest.

Its coverage ranges from fundamental principles to practical implementation, reflecting the multidimensional facets of interests and concerns of the community.

Its mission is to bring up-to-date, emerging and active technical developments, issues, and events to the research, educational, and professional communities.

It is also the main society communication platform addressing important issues concerning all members.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	9.6

Journal Rank

Year	Value
2024	865

Journal Citation Indicator

Year	Value
2024	2506

SJR (SCImago Journal Rank)

Year	Value
2024	2.685

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	219

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, Engineering and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• An Introduction To Compressive Sampling

  Citation: 7967

  Authors: E.J., M.B.

• Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

  Citation: 7536

  Authors: Geoffrey, Li, Dong, George, Abdel-rahman, Navdeep, Andrew, Vincent, Patrick, Tara, Brian

• Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays

  Citation: 4379

  Authors: F., D., E. G., T. L., F.

• Two decades of array signal processing research: the parametric approach

  Citation: 3735

  Authors: H., M.

• Compressive Sensing [Lecture Notes]

  Citation: 3716

  Authors: Richard

• Federated Learning: Challenges, Methods, and Future Directions

  Citation: 3672

  Authors: Tian, Anit Kumar, Ameet, Virginia

• The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]

  Citation: 3625

  Authors:

• Generative Adversarial Networks: An Overview

  Citation: 3424

  Authors: Antonia, Tom, Vincent, Kai, Biswa, Anil A.

• Deep Reinforcement Learning: A Brief Survey

  Citation: 3333

  Authors: Kai, Marc Peter, Miles, Anil Anthony

• The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains

  Citation: 3253

  Authors: D. I., S. K., P., A., P.

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