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

Integrated sensing and communications (ISAC) are envisioned to be an integral part of future wireless networks, especially when operating at the millimeter-wave (mm-wave) and terahertz (THz) frequency bands. However, establishing wireless connections at these high frequencies is quite challenging, mainly due to the penetrating path loss that prevents reliable communication and sensing. Another emerging

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

In this “Lecture Notes” column, we show that it is possible to use deterministic arguments to gain some intuition into why using periodograms without averaging does not work well and why they “fail” in the way they do. We then explain how the probabilistic case can be seen as an extension of the deterministic case. Next, we give a brief description of the method of averaged periodograms and explain

The possibility of manipulating digital multimedia material is nowadays within everyone’s reach. In the audio case, anybody can create fake synthetic speech tracks using various methods with almost no effort [1] . These methods range from simple waveform concatenation operations to more complex neural networks [2] , [3] .

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

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

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Nikola Tesla said: “If you want to find the secrets of the universe, think in terms of energy, frequency and vibration.” Unfortunately, this is a hieroglyph, and we are still looking for its Rosetta Stone.

Quaternions are still largely misunderstood and often considered an “exotic” signal representation without much practical utility despite the fact that they have been around the signal and image processing community for more than 30 years now. The main aim of this article is to counter this misconception and to demystify the use of quaternion algebra for solving problems in signal and image processing

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

Inference tasks in signal processing are often characterized by the availability of reliable statistical modeling with some missing instance-specific parameters. One conventional approach uses data to estimate these missing parameters and then infers based on the estimated model. Alternatively, data can also be leveraged to directly learn the inference mapping end to end. These approaches for combining

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.

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

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

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

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

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

In multidimensional signal processing, such as image and video processing, superresolution (SR) imaging is a classical problem. Over the past 25 years, academia and industry have been interested in reconstructing high-resolution (HR) images from their low-resolution (LR) counterparts. We review the development of SR technology in this tutorial article based on the evolution of key insights associated

Information forensics and security (IFS) is an active R&D area whose goal is to ensure that people use devices, data, and intellectual properties for authorized purposes and to facilitate the gathering of solid evidence to hold perpetrators accountable. For over a quarter century, since the 1990s, the IFS research area has grown tremendously to address the societal needs of the digital information

Brain–computer interfaces (BCIs) employ neurophysiological signals derived from the brain to control computers or external devices. By enhancing or replacing human peripheral functioning capacity, BCIs offer supplementary degrees of freedom, significantly improving individuals’ quality of life, particularly offering hope for those with locked-in syndrome (LIS). Moreover, BCI applications have expanded

This article reviews significant advances in networked signal and information processing (SIP), which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decisions and actions. Moreover, and significantly

Radar and communications (R&C) as key utilities of electromagnetic (EM) waves have fundamentally shaped human society and triggered the modern information age. Although R&C had been historically progressing separately, in recent decades, they have been converging toward integration, forming integrated sensing and communication (ISAC) systems, giving rise to new highly desirable capabilities in next-generation

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

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

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

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

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. These immersive conference experiences, to which attendees travel from all over the world to be together for a set period of

Let me begin by telling you that 1969 was a great year—a really, really, really great year.

Signal processing (SP) excels at analyzing, processing, and inferring information defined over regular (first continuous, later discrete) domains such as time or space. Indeed, the last 75 years have shown how SP has made an impact in areas such as communications, acoustics, sensing, image processing, and control, to name a few. With the digitalization of the modern world and the increasing pervasiveness

The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing. Areas of interest include medical and biological imaging, digital pathology, molecular imaging, microscopy, and associated computational imaging, image analysis, and image-guided treatment, alongside

Multimedia signal processing (MMSP) refers to processing of signals from multiple media—speech, audio, images, text, graphics, point clouds, etc.—often jointly. This article reviews the history of MMSP and, in parallel, the history of the MMSP Technical Committee (TC), with a focus on the last three decades ( Figure 1 ).

In this article, a general introduction to the area of sensor array and multichannel signal processing is provided, including associated activities of the IEEE Signal Processing Society (SPS) Sensor Array and Multichannel (SAM) Technical Committee (TC). The main technological advances in five SAM subareas made in the past 25 years are then presented in detail, including beamforming, direction-of-arrival

The signal processing community is currently witnessing the emergence of sensor array processing and direction-of-arrival (DoA) estimation in various modern applications, such as automotive radar, mobile user and millimeter wave indoor localization, and drone surveillance, as well as in new paradigms, such as joint sensing and communication in future wireless systems. This trend is further enhanced

Wireless communication technology has progressed dramatically over the past 25 years, in terms of societal adoption as well as technical sophistication. In 1998, mobile phones were still in the process of becoming compact and affordable devices that could be widely utilized in both developed and developing countries. There were “only” 300 million mobile subscribers in the world [1] . Cellular networks

Beamforming is a signal processing technique to steer, shape, and focus an electromagnetic (EM) wave using an array of sensors toward a desired direction. It has been used in many engineering applications, such as radar, sonar, acoustics, astronomy, seismology, medical imaging, and communications. With the advent of multiantenna technologies in, say, radar and communication, there has been a great

Je crois invinciblement que la science et la paix triompheront de l’ignorance et de la guerre ( I believe invincibly that science and peace will triumph over ignorance and war )

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

Humans can listen to a target speaker even in challenging acoustic conditions that have noise, reverberation, and interfering speakers. This phenomenon is known as the cocktail party effect . For decades, researchers have focused on approaching the listening ability of humans. One critical issue is handling interfering speakers because the target and nontarget speech signals share similar characteristics

Apollo 11 was the first manned space mission to successfully bring astronauts to the Moon and return them safely. As part of NASA’s goal in assessing team and mission success, all voice communications within mission control, astronauts, and support staff were captured using a multichannel analog system, which until recently had never been made available. More than 400 personnel served as mission specialists/support

Linear regression models have a wide range of applications in statistics, signal processing, and machine learning. In this Lecture Notes column we will examine the performance of the least-squares (LS) estimator with a focus on the case when there are more parameters than training samples, which is often overlooked in textbooks on estimation.

Analyzing the magnitude response of a finite-length sequence is a ubiquitous task in signal processing. However, the discrete Fourier transform (DFT) provides only discrete sampling points of the response characteristic. This work introduces bounds on the magnitude response, which can be efficiently computed without additional zero padding. The proposed bounds can be used for more informative visualization

Cascaded integrator-comb (CIC) decimators provide natural aliasing rejection in folding bands. However, improving that rejection requires increasing the number of integrator-comb pairs, N , and this affects the attractive simplicity of the CIC system as N grows. For a given N , the worst-case attenuation can still be improved by applying zero rotations. However, state-of-the-art zero-rotation methods

High-performance filtering (a flat passband, a sharp transition band, and a highly suppressed stopband) is always the ultimate goal of any digital signal processing (DSP) practitioner. However, high-performance filtering is traditionally the synonym of high implementation complexity. In this article, we propose an approach to reach high-performance filtering with lower implementation complexity. This

The fashion industry is on the verge of an unprecedented change. Fashion applications are benefiting greatly from the development of machine learning, computer vision, and artificial intelligence. In this article, we present an overview of three major topics of fashion and associated state-of-the-art techniques: 1) fashion analysis, including popularity prediction and fashion trend analysis; 2) fashion

Dr. Harry L. Van Trees passed away peacefully on 29 December 2022 with his wife of 69 years, Diane, and his family by his side. He had a distinguished career that spanned a variety of academic, government, and industry positions, and he is widely recognized as one of the founders of the detection and estimation theory body of knowledge. His four-volume series of textbooks, Detection, Estimation, and

Professor Enders Anthony Robinson, Ph.D., passed away in his sleep at the age of 92 on 6 December 2022. He was regarded as the “father of digital geophysics,” and his deconvolution methods have advanced a wide range of signal processing applications.

In previous editorials, SPS President Athina Petropulu and I had the opportunity to say a few words about ethics, especially taking into account the usefulness of our research projects, for humanity and Earth, in a wide sense. In the current energy crisis and the explosion of costs, this issue becomes still more important, and I believe that it must be considered carefully in all our projects. Scientific

As I am writing this article, I am wrapping up a trip as IEEE Signal Processing Society (SPS) president to Doha, Qatar (9–11 January), to speak at the 2022 IEEE Spoken Language Technology (SLT) Workshop, and India (12–16 January), for technical talks and meetings with local signal processing researchers and SPS local Chapter chairs.

Thanks to the tremendous interest from the research community, the focus of the March issue of the IEEE Signal Processing Magazine is on the second volume of the special issue on physics-driven machine learning for computational imaging, which brings together nine articles of the 19 accepted papers from the original 47 submissions.

Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It is an ill-posed inverse problem whose solution is usually computationally expensive. Machine learning (ML) techniques and especially deep learning (DL) show potential in fast and accurate imaging. However, the high performance of purely data-driven approaches relies on constructing

Visualizing information inside objects is an everlasting need to bridge the world from physics, chemistry, and biology to computation. Among all tomographic techniques, terahertz (THz) computational imaging has demonstrated its unique sensing features to digitalize multidimensional object information in a nondestructive, nonionizing, and noninvasive way. Applying modern signal processing and physics-guided

Phase retrieval in optical imaging refers to the recovery of a complex signal from phaseless data acquired in the form of its diffraction patterns. These patterns are acquired through a system with a coherent light source that employs a diffractive optical element (DOE) to modulate the scene, resulting in coded diffraction patterns (CDPs) at the sensor. Recently, the hybrid approach of a model-driven

Spectral unmixing is central when analyzing hyperspectral data. To accomplish this task, physics-based methods have become popular because, with their explicit mixing models, they can provide a clear interpretation. Nevertheless, because of their limited modeling capabilities, especially when analyzing real scenes with unknown complex physical properties, these methods may not be accurate. On the other

Computational optical imaging (COI) systems leverage optical coding elements (CEs) in their setups to encode a high-dimensional scene in a single or in multiple snapshots and decode it by using computational algorithms. The performance of COI systems highly depends on the design of its main components: the CE pattern and the computational method used to perform a given task. Conventional approaches

Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction networks often suffer from the black-box nature and major issues, such as instabilities, which are major barriers to applying DL methods in LDCT applications. An emerging

Magnetic resonance spectroscopic imaging (MRSI) offers a unique molecular window into the physiological and pathological processes in the human body. However, the applications of MRSI have been limited by a number of long-standing technical challenges due to the high dimensionality and low signal-to-noise ratio (SNR). Recent technological developments integrating physics-based modeling and data-driven

Quantitative magnetic resonance imaging (qMRI) aims to obtain quantitative biophysical parameters based on physical models derived from MR spin magnetization evolution. This requires the acquisition of multiple MR images, resulting in very long scan times. Recently, deep learning (DL) has shown significant potential for reconstructing undersampled MR data. When applying DL to fast qMRI, physical priors

Deep learning (DL) has driven innovation in the field of computational imaging. One of its bottlenecks is unavailable or insufficient training data. This article reviews an emerging paradigm, imaging physics-based data synthesis (IPADS), that can provide huge training data in biomedical magnetic resonance (MR) without or with few real data. Following the physical law of MR, IPADS generates signals

A computational experiment is deemed reproducible if the same data and methods are available to replicate quantitative results by any independent researcher, anywhere and at any time, granted that they have the required computing power. Such computational reproducibility is a growing challenge that has been extensively studied among computational researchers as well as within the signal processing