IEEE Signal Processing Society

From the Editor

blog_0623_header_general.jpg

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.

Our Fall Flagship Event: A Story of Past Accomplishments and Proposed Innovations

President's Message

abstract_general_5.jpg

I am writing this short note as I am about to board a plane to Abu Dhabi to join those of you who are attending the 2024 edition of the International Conference on Image Processing (ICIP 2024). The team organizing ICIP 2024 has put together an outstanding technical program that includes world-class plenary speakers discussing research and industrial trends.

Neural Kalman Filters for Acoustic Echo Cancellation: Comparison of deep neural network-based extensions

SPM Articles

signals_general.jpg

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.

Special Issue on Model-Based and Data-Driven Audio Signal Processing

SPM Articles

network2_general.jpg

“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.

Microphone Array Signal Processing and Deep Learning for Speech Enhancement: Combining model-based and data-driven approaches to parameter estimation and filtering

SPM Articles

July22SPM_general.jpg

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.

Wideband Sensor Resource Allocation for Extended Target Tracking and Classification

Communication base stations can achieve high-precision tracking and accurate classification for multiple extended targets in the context of integrated communication and sensing by transmitting wideband signal. However, the time resources of the base stations are often limited. In the time-division operation mode, part of the time resources must be reserved to guarantee communication performance, while the rest of the resources must be properly allocated for better multi-target sensing performance.

Robust Phase Retrieval by Alternating Minimization

We consider a least absolute deviation (LAD) approach to the robust phase retrieval problem that aims to recover a signal from its absolute measurements corrupted with sparse noise. To solve the resulting non-convex optimization problem, we propose a robust alternating minimization (Robust-AM) derived as an unconstrained Gauss-Newton method.

Byzantine-Robust and Communication-Efficient Personalized Federated Learning

This paper explores constrained non-convex personalized federated learning (PFL), in which a group of workers train local models and a global model, under the coordination of a server. To address the challenges of efficient information exchange and robustness against the so-called Byzantine workers, we propose a projected stochastic gradient descent algorithm for PFL that simultaneously ensures Byzantine-robustness and communication efficiency.

Reliable Robust Adaptive Steganographic Coding Based on Nested Polar Codes