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CURRENT ISSUE
CURRENT ISSUE
January 2025
Near-Field Signal Processing: Unleashing the power of proximity
After nearly a century of specialized applications in optics, remote sensing, and acoustics, the near-field (NF) electromagnetic (EM) propagation zone is experiencing a resurgence in research interest. This renewed attention is fueled by the emergence of promising applications in various fields, such as wireless communications, holography, medical imaging, and quantum-inspired systems.
Looking Back on My First Year as Editor-in-Chief and Reflecting on the Challenges Ahead
One year has passed since I began my term as the editor-in-chief (EiC) of IEEE Signal Processing Magazine (SPM). It has been a busy first year, with a rich set of challenges that go beyond those I have experienced in previous volunteer positions. This is welcome: with giving back to our community comes the desire to grow through new challenges and experiences, especially for those of us approaching our wiser years.
November 2024
An Exciting Juncture: The Convergence of Machine Learning and Signal Processing
This is our sixth and final issue of 2024. It is hard to believe that a year has gone by since our term as the new editorial team started in January. In our first year, in addition to our usual array of technical overviews and Society news, we addressed a number of topics of significance for our community in the hopes of starting a discussion.
Neural Kalman Filters for Acoustic Echo Cancellation: Comparison of deep neural network-based extensions
Multichannel acoustic signal processing is a well-established and powerful tool to exploit the spatial diversity between a target signal and nontarget or noise sources for signal enhancement. However, the textbook solutions for optimal data-dependent spatial filtering rest on the knowledge of second-order statistical moments of the signals, which have traditionally been difficult to acquire.
Microphone Array Signal Processing and Deep Learning for Speech Enhancement: Combining model-based and data-driven approaches to parameter estimation and filtering
Multichannel acoustic signal processing is a well-established and powerful tool to exploit the spatial diversity between a target signal and nontarget or noise sources for signal enhancement. However, the textbook solutions for optimal data-dependent spatial filtering rest on the knowledge of second-order statistical moments of the signals, which have traditionally been difficult to acquire.
