IEEE Signal Processing Magazine

2.1k papers and 171.3k indexed citations i.

About

The 2.1k papers published in IEEE Signal Processing Magazine in the last decades have received a total of 171.3k indexed citations. Papers published in IEEE Signal Processing Magazine usually cover Computer Vision and Pattern Recognition (561 papers), Signal Processing (493 papers) and Artificial Intelligence (466 papers) specifically the topics of Blind Source Separation Techniques (146 papers), Speech and Audio Processing (142 papers) and Image and Signal Denoising Methods (142 papers). The most active scholars publishing in IEEE Signal Processing Magazine are Richard G. Baraniuk, Michael B. Wakin, Emmanuel J. Candès, Li Deng, Todd K. Moon, Petre Stoica, Hamid Krim, Mats Viberg, John F. Mustard and N. Keshava.

In The Last Decade

Fields of papers published in IEEE Signal Processing Magazine

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in IEEE Signal Processing Magazine. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in IEEE Signal Processing Magazine.

Countries where authors publish in IEEE Signal Processing Magazine

Since Specialization
Citations

This map shows the geographic impact of research published in IEEE Signal Processing Magazine. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in IEEE Signal Processing Magazine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites IEEE Signal Processing Magazine more than expected).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar’s output or impact.

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