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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.

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.

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.

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.

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.

As we gear up for the International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2024, it is essential to take a moment to celebrate the achievements and highlights of ICASSP 2023, which took place on Rhodes Island, Greece, this past June. ICASSP 2023 was a momentous event as it marked the first postpandemic ICASSP, and the return to in-person meetings. With the theme “Signal Processing in the AI Era,” the conference underscored the strong connection between signal processing and machine learning, highlighting the pivotal role of signal processing in shaping the development of artificial intelligence (AI).

The objectives of IEEE Signal Processing Magazine ( SPM ) are to propose, for any IEEE Signal Processing Society (SPS) member and beyond, a wide range of tutorial articles on both methods and applications in signal and image processing. The articles are divided into different categories: feature articles, column and forum articles, and articles in special issues, the specificities of which are detailed on the SPM webpage “Information for Authors - SPM”.

The ICASSP 2023 conference in Rhodes, Greece, was remarkable from multiple perspectives. Notably, this was the first fully in-person ICASSP after three consecutive virtual conferences, which were necessitated by the COVID-19 pandemic. Attendees fully embraced the opportunity to engage in live interactions and reestablish their networks.

The 75th anniversary of the IEEE Signal Processing Society (SPS) is an ideal time to look at the rapid advances in our field and the many ways that these increasingly powerful technologies have transformed our professions and the world. This is not just a time to celebrate past achievements and pat ourselves on the back, but also to educate young students and innovators about the history of our profession, the challenges we have overcome, and the breakthroughs that have led to the incredible growth of Signal Processing (SP).

The underrepresentation of women in science, technology, engineering, and mathematics (STEM) fields is an issue that has been studied extensively [1] . Yet women still face many challenges, even though the demand for many STEM occupations has exploded. Many factors contribute to the low number of women in the STEM field. From an early age, girls are exposed to many cultural cues that dissuade them from participating in STEM fields. This gender bias is enforced by implicit or explicit messages from multiple sources.



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