Prof. Dr.-Ing. Marius Pesavento has been appointed Editor-in-Chief for the IEEE Open Journal of Signal Processing. He is succeeding Brendt Wohlberg, who has held the post of Editor-in-Chief since 2022.
The IEEE Open Journal of Signal Processing (OJ-SP) is a fully gold open-access, peer-reviewed journal of the IEEE Signal Processing Society. Launched in 2020 as part of IEEE’s broader initiative to establish high-quality open-access journals, OJ-SP publishes original research spanning signal processing theory, methods, and applications. The journal combines rigorous IEEE editorial standards with immediate open access to support wide dissemination and compliance with global open-access mandates.
The journal covers the enabling technologies for the generation, transformation, extraction, and interpretation of information. Its scope includes signal processing theory; algorithms and associated architectures and implementations; and applications involving information represented in diverse signal formats. Contributions may draw on mathematical, statistical, computational, heuristic, and linguistic approaches to model, process, transmit, and learn from signals. OJ-SP publishes research that advances foundational knowledge as well as work that addresses emerging challenges and applications across the spectrum of signal processing.
In addition to regular journal articles, OJ-SP has successfully supported short-paper initiatives in close coordination with IEEE Signal Processing Society flagship conferences notably IEEE ICASSP and IEEE ICIP. These tracks provide a journal-quality, open-access publication pathway for selected conference submissions, combining rigorous peer review with rapid dissemination and helping to bridge the gap between conference and journal publishing within the SPS community. This model enables researchers to achieve archival journal publication while benefiting from the visibility and timeliness of the associated conference.
OJ-SP has also been at the forefront of reproducibility efforts, including the integration of code review for learning-based papers. In this process, authors provide a runnable code capsule (e.g., via Code Ocean) that reviewers use to validate computational results alongside the manuscript. This reproducibility review improves transparency, verifiability, and confidence in empirical findings, particularly for data-driven and machine learning-based signal processing research. Upon publication, the reviewed code capsule is made publicly available and linked with a DOI, further supporting open science.
To support the timely communication of research advances, OJ-SP places strong emphasis on a rapid peer-review and publication process. The journal aims for an efficient review cycle, with a typical target of approximately 15 weeks from initial submission to publication, reflecting its commitment to responsiveness and timely dissemination of high-quality research.
