What Is IEEE Signal Processing Magazine?

IEEE Signal Processing Magazine is a bimonthly publication of the IEEE Signal Processing Society, distributed to all society members and serving as the primary communication channel between the society and its global community of researchers, engineers, and educators. Unlike conventional technical journals that focus on reporting original research results, the magazine specializes in tutorial-style articles that survey mature areas, explain emerging concepts, and connect foundational theory with practical application. Its editorial mission is to keep practitioners and researchers current with active developments across the full breadth of signal processing and its adjacent fields.

The magazine traces its origins to the IEEE ASSP Magazine, published under the IEEE Acoustics, Speech, and Signal Processing Society. When that society reorganized and became the IEEE Signal Processing Society in 1990, the publication was retitled accordingly. Over the decades it has maintained an impact factor of 10.3 and a CiteScore of 20.5, reflecting its standing as one of the most widely read periodicals in the signal processing field.

Tutorial Content and Special Issues

The core editorial product of the magazine is the tutorial article: a long-form treatment of an important theory, algorithm, or application area written to be accessible to practitioners who are not specialists in the specific sub-field. Special issues gather multiple tutorial articles around a central theme, such as sparse signal recovery, machine learning for communications, or computational imaging, and represent the most-cited content in any given volume. Feature articles address current topics of broad relevance and are published outside the special-issue structure, giving the magazine flexibility to respond to fast-moving developments. Each article type is subject to peer review, and editors actively solicit contributions from researchers recognized as leading voices in their areas.

Columns and Forums

Alongside full-length articles, the magazine publishes a set of standing columns and forum sections that address topics outside the scope of traditional research reporting. Columns cover digital signal processing practice, signal processing education, industry perspectives, and historical retrospectives on foundational work in the field. Forum pieces provide a venue for commentary on professional issues, standards activity, and emerging areas where the community is still forming consensus. This structure distinguishes the magazine from IEEE Transactions publications, which are confined to original research, and gives it an ongoing voice in disciplinary conversations about where signal processing is headed and how it is taught. The IEEE Xplore digital library archives all issues from the magazine's founding, making the full column record available for research.

Reproducible Research and Multimedia Content

In line with open-science practices, the magazine requires authors to share the code, datasets, and supplementary materials associated with their articles in accessible online repositories. This reproducibility policy, which is more stringent than those of many companion IEEE publications, reflects the practical orientation of the readership: signal processing engineers frequently want to implement and verify the algorithms described in tutorial articles rather than simply read about them. Authors may also submit multimedia content alongside traditional text, including MATLAB code packages, audio samples, image sets, and video demonstrations. The IEEE Signal Processing Society's author guidelines specify the technical requirements for these supplementary materials.

Applications

IEEE Signal Processing Magazine covers signal processing research and methods applied across a range of disciplines, including:

• Audio and speech processing for telecommunications and consumer devices
• Biomedical signal analysis in imaging and diagnostic systems
• Radar, sonar, and remote sensing in defense and environmental monitoring
• Machine learning and statistical inference in data-intensive applications
• Image and video processing for computer vision and multimedia systems