IEEE Signal Processing Magazine

Deep learning (DL) has been wildly successful in practice, and most of the state-of-the-art machine learning methods are based on neural networks (NNs). Lacking, however, is a rigorous mathematical theory that adequately explains the amazing performance of deep NNs (DNNs). In this article, we present a relatively new mathematical framework that provides the beginning of a deeper understanding of DL. This framework precisely characterizes the functional properties of NNs that are trained to fit to data. The key mathematical tools that support this framework include transform-domain sparse regularization, the Radon transform of computed tomography, and approximation theory, which are all techniques deeply rooted in signal processing.

Compression is essential for efficient storage and transmission of signals. One powerful method for compression is through the application of orthogonal transforms, which convert a group of N data samples into a group of N transform coefficients. In transform coding, the N samples are first transformed, and then the coefficients are individually quantized and entropy coded into binary bits. The transform serves two purposes: one is to compact the energy of the original N samples into coefficients with increasingly smaller variances so that removing smaller coefficients have negligible reconstruction errors, and another is to decorrelate the original samples so that the coefficients can be quantized and entropy coded individually without losing compression performance. 

Twenty-five years ago, the field of computational imaging arguably did not exist, at least not as a standalone arena of research activity and technical development. Of course, the idea of using computation to form images had been around for several decades, largely thanks to the development of medical imaging—such as magnetic resonance imaging (MRI) and X-ray tomography - in the 1970s and synthetic-aperture radar (SAR) even earlier. 

In this article, we summarize the evolution of speech and language processing (SLP) in the past 25 years. We first provide a snapshot of popular research topics and the associated state of the art (SOTA) in various subfields of SLP 25 years ago, and then highlight the shift in research topics over the years. We describe the major breakthroughs in each of the subfields and the main driving forces that led us to the SOTA today. Societal impacts and potential future directions are also discussed.

Audio signal processing has passed many landmarks in its development as a research topic. Many are well known, such as the development of the phonograph in the second half of the 19th century and technology associated with digital telephony that burgeoned in the late 20th century and is still a hot topic in multiple guises. Interestingly, the development of audio technology has been fueled not only by advancements in the capabilities of technology but also by high consumer expectations and customer engagement.

It is our great pleasure to introduce the second part of this special issue to you! The IEEE Signal Processing Society (SPS) has completed 75 years of remarkable service to the signal processing community. The eight selected articles included in this second part are clear portraits of that. As the review process for these articles took longer, however, they could not be included in the first part of the special issue, and we are glad to bring them to you now.

It is our great pleasure to introduce the first part of this special issue to you! The IEEE Signal Processing Society (SPS) has completed 75 years of remarkable service to the signal processing community. When the Society was founded in 1948, we couldn’t imagine, for instance, how wireless networks of smartphones would be able to connect us easily at all times, or that an image processing algorithm would be able to detect cancer in a few seconds.

Signal processing (SP) is a “hidden” technology that has transformed the digital world and changed our lives in so many ways. The field of digital SP (DSP) took off in the mid-1960s, aided by the integrated circuit and increasing availability of digital computers. Since then, the field of DSP has grown tremendously and fueled groundbreaking advances in technology across a wide range of fields with profound impact on society. 

When I began writing this 75th anniversary article celebrating women in signal processing (SP), I reread the 1998 editorial titled “Fifty Years of Signal Processing: 1948–1998” [1] . At that time, IEEE had more than 300,000 members in 150 nations, the world’s largest professional technical Society. Within the IEEE umbrella, there were 37 IEEE Societies and technical groups, and the IEEE Signal Processing Society (SPS) was the oldest among its many Societies.

Throughout the IEEE Signal Processing Society’s (SPS’s) history, conferences have functioned as a main way to connect within the Society, bringing together the signal processing research community to discuss and debate, establish research collaborations, and have a good time.