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CURRENT ISSUE
CURRENT ISSUE
January 2025
Special Issue on Near-Field Signal Processing: Communications, Sensing, and Imaging
Multichannel signal processing technologies are moving toward the deployment of small and densely packed sensors yielding extremely large aperture arrays (ELAAs) in order to provide higher angular resolution and beamforming gain. In particular, technologies are moving beyond the fifth-generation (5G) networks, wherein the adoption of ELAAs or surfaces and the exploitation of higher-frequency bands, e.g., terahertz
IEEE Signal Processing Society: State of the Society
As we enter the new year, I would like to take a moment to reflect on our achievements from the past year and celebrate our successes. I am naming this “President’s Message” column the “State of the Society,” which will serve as a platform to share updates about our Society, including our activities, successes, and challenges.
November 2024
Special Issue on Model-Based and Data-Driven Audio Signal Processing
“All models are wrong, but some are useful” - understanding “models” as analytical mathematical models, this aphorism, originating from George Box in 1976, motivates the synthesis of model-based and data-driven audio signal processing as the leitmotif of this special issue.
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.
