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Section: Publications & Resources

This year, all eligible IEEE Signal Processing Society Members were able to cast their vote for the next IEEE Signal Processing Society President-Elect. The President-Elect will sit on the IEEE Signal Processing Society Board of Governors beginning 1 January 2024 and will serve until 31 December 2025, when they will be elevated to Society President.

Section: Publications & Resources

The IEEE Signal Processing Society (SPS) is proud to announce the inaugural class of SPS Scholarship recipients! New in 2023, the SPS Scholarship Program recognizes students who have expressed interest and commitment to pursuing signal processing education and real-world career experiences. 

Section: Publications & Resources

The Signal Processing Society (SPS) conducts webinars presented by professionals in the field of signal processing and related technologies on an ongoing basis. Webinars are also hosted periodically by SPS Technical Committees, and other network of communities. Visit the Upcoming Events page to see a list of our upcoming webinars and join us! Visit the SPS BLOG for more related content on a regular basis!

Section: Publications & Resources

Publication: IEEE Journal of Selected Topics in Signal Processing

Manuscript Due: 30 April 2024 Publication Date: January 2025

Section: Publications & Resources

Publication: IEEE Journal of Selected Topics in Signal Processing

Deadline approaching! Submit now to the IEEE JSTSP Special Issue on Seeking Low-dimensionality in Deep Neural Networks (SLowDNN). Submission Deadline: 30 November 2023. 

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

My end of term as IEEE Signal Processing Society (SPS) president is fast approaching. It has been an incredible experience that has provided me with so many opportunities to engage with our members around the globe, forge relationships with other IEEE Societies, and meet a diverse range of people that I hope will become active members of our Society in the future. It has been a great privilege to be at the helm of a Society that garners such a high level of worldwide respect and recognition.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

My three years of service as the editor-in-chief (EIC) of Signal Processing Magazine ( SPM ) are now coming to a close. During the past three years, many of us were deeply affected by serious political, social, and environmental events such as the war in Ukraine; protests for freedom in Iran; coups d’état in Africa; the COVID-19 pandemic; seisms in Turkey, Syria, and Morocco; huge floods in Libya and India; gigantic fires in North America and Southern Europe; and an avalanche of stones in the Alps, to name a few. In such a context, I believe that the IEEE slogan, “Advancing Technology for Humanity,” is incredibly relevant and timely.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

Joseph Fourier’s methods (and their variants) are omnipresent in audio signal processing. However, it turns out that the underlying ideas took some time to penetrate the field of sound analysis and that different paths were first followed in the period immediately following Fourier’s pioneering work, with or without reference to him. This illustrates the interplay between mathematics and physics as well as the key role played by instrumentation, with notable inventions by outsiders to academia, such as Rudolph Koenig and Édouard-Léon Scott de Martinville.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

Designing filters with perfect frequency responses (i.e., flat passbands, sharp transition bands, highly suppressed stopbands, and linear phase responses) is always the ultimate goal of any digital signal processing (DSP) practitioner. High-order finite impulse response (FIR) filters may meet these requirements when we put no constraint on implementation complexity. In contrast to FIR filters, infinite impulse response (IIR) filters, owing to their recursive structures, provide an efficient way for high-performance filtering at reduced complexity.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

Encoding-decoding convolutional neural networks (CNNs) play a central role in data-driven noise reduction and can be found within numerous deep learning algorithms. However, the development of these CNN architectures is often done in an ad hoc fashion and theoretical underpinnings for important design choices are generally lacking. Up to now, there have been different existing relevant works that have striven to explain the internal operation of these CNNs. Still, these ideas are either scattered and/or may require significant expertise to be accessible for a bigger audience.