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The IEEE Signal Processing Society annual election will commence on 17 October, and your vote is more important than ever! Ballots must be received at the IEEE no later than 4 December 2025.

The IEEE Signal Processing Society (SPS) announces the 2026 Class of Distinguished Lecturers and Distinguished Industry Speakers for the term of 1 January 2026 to 31 December 2027, which are noted below. The IEEE SPS Distinguished Lecturer (DL) Program provides a means for Chapters to have access to well-known educators and authors in the fields of signal processing to lecture at Chapter meetings. While many IEEE Societies have similar programs, the SPS provides a substantial amount of financial support for the Chapters to take advantage of this service.

Section: Publications & Resources

Deadline has been extened to submit your proposal today to be considered for funding for events being held during April-September 2026

IEEE SPS Short Course Bundles cover the latest innovations in Computational Imaging, Wireless AI, and Deep Learning.

Data-driven approaches have swept all walks of science and engineering in recent years, with deep neural networks, deep reinforcement learning, and adversarial networks becoming the new staples that everyone uses to tackle a very wide variety of problems. While the empirical success of these methods is truly impressive when a lot of training data are available, there are still many problems that can, in fact, benefit from classical machine learning tools.

Unmanned aerial vehicles (UAVs) or drones have found numerous applications in wireless communication, as either an aerial user terminal or a mobile access point (AP). Compared to conventional terrestrial wireless systems, UAVs' communications face new challenges due to their high altitude above the ground and great flexibility of movement.

Sparse approximation is a well-established theory, with a profound impact on the fields of signal and image processing. In this talk, we start by presenting this model and its features, and then turn to describe two special cases from it: 1) the convolutional sparse coding (CSC) and 2) its multi-layered version (ML-CSC). Amazingly, as we will carefully show, ML-CSC provides a solid theoretical foundation to deep-learning architectures.

Thanks to the introduction of deep networks for representation learning, the past few years have seen a dramatic increase in the performance of recognition systems. However, the mathematical reasons for this success remain elusive. For example, a key issue is that the neural network training problem is nonconvex, hence, optimization algorithms may not return a global minima. In addition, the regularization properties of algorithms such as dropout remain poorly understood.

Plenary talk delivered at ICIP 2019 in Taipei.

A person’s speech is strongly conditioned by his own articulators and the language(s) he speaks, hence, rendering speech in an interspeaker or interlanguage manner from a source speaker’s speech data collected in his native language that is both academically challenging and technology/application desirable. The quality of the rendered speech is assessed in three dimensions: naturalness, intelligibility, and similarity to the source speaker. Usually, the three criteria cannot be all met when rendering is done in both cross-speaker and cross-language ways.