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A warm greeting to the signal processing community as I start my term as the editor-in-chief of IEEE Signal Processing Magazine (SPM). I hope to be worthy of the confidence invested in me and to be able to follow successfully in Christian Jutten’s footsteps. He led our magazine for three years with dedication and brought timely topics like green signal processing, ethics, and reproducibility to the attention of our community. I certainly have big shoes to fill!

In the first issue of 2024, we introduced the new lead editorial team of IEEE Signal Processing Magazine (SPM), composed of our four area editors. Their terms started with mine this January, and they oversee the Society e-newsletter and the three main components of our magazine: feature articles, special issues, and columns and forum articles. As a team, we have undertaken a complete revision of the specifications for all article types and the information we provide our authors. We also revised the templates for all article types along with proposals and white papers, and all are included within the IEEE Author Center’s template selector [1].

For reasons beyond our control, the issues of IEEE Signal Processing Magazine arrive to you with delays this year. As you receive the current March issue, we are back from another edition of our flagship conference, the IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP), which took place in Seoul, Korea, 14–19 April 2024. It was successful and vibrant, and, with 4,432 attendees and 2,826 accepted papers (out of 5,896 submitted), it was bigger than ever. At the risk of being labeled a grumpy Muppet, I will note that ICASSPs are now a tad too big for me, as I often found myself at a loss trying to choose among a seemingly endless number of attractive sessions and events at any given time. Of course, we still have our workshops, which are intimate and focused, and a number of them are even single tracks.

Objective: Brain function is understood to be regulated by complex spatiotemporal dynamics, and can be characterized by a combination of observed brain response patterns in time and space. Magnetoencephalography (MEG), with its high temporal resolution, and functional magnetic resonance imaging (fMRI), with its high spatial resolution, are complementary imaging techniques with great potential to reveal information about spatiotemporal brain dynamics. Hence, the complementary nature of these imaging techniques holds much promise to study brain function in time and space, especially when the two data types are allowed to fully interact. Methods: We employed coupled tensor/matrix factorization (CMTF) to extract joint latent components in the form of unique spatiotemporal brain patterns that can be used to study brain development and function on a millisecond scale. Results: Using the CMTF model, we extracted distinct brain patterns that revealed fine-grained spatiotemporal brain dynamics and typical sensory processing pathways informative of high-level cognitive functions in healthy adolescents. The components extracted from multimodal tensor fusion possessed better discriminative ability between high- and low-performance subjects than single-modality data-driven models. Conclusion: Multimodal tensor fusion successfully identified spatiotemporal brain dynamics of brain function and produced unique components with high discriminatory power. Significance: The CMTF model is a promising tool for high-order, multimodal data fusion that exploits the functional resolution of MEG and fMRI, and provides a comprehensive picture of the developing brain in time and space.

This paper addresses the problem of robust Gaussian mixture modeling in the presence of outliers. We commence by introducing a general expectation-maximization (EM)-like scheme, called $\mathcal{K}$-BM, for iterative numerical computation of the minimum $\mathcal{K}$-divergence estimator (M$\mathcal{K}$DE). This estimator leverages Parzen's non-parametric $\mathcal{K}$ernel density estimate to down-weight low density regions associated with outlying measurements. Akin to the conventional EM, the $\mathcal{K}$-BM involves successive Maximizations of lower Bounds on the objective function of the M$\mathcal{K}$DE. However, differently from EM, these bounds are not exclusively reliant on conditional expectations. The $\mathcal{K}$-BM algorithm is applied to robust parameter estimation of a finite-order multivariate Gaussian mixture model (GMM). We proceed by introducing a new robust variant of the Bayesian information criterion (BIC) that penalizes the M$\mathcal{K}$DE's objective function. The proposed criterion, called $\mathcal{K}$-BIC, is conveniently applied for robust GMM order selection. In the paper, we also establish a data-driven procedure for selection of the kernel's bandwidth parameter. This procedure operates by minimizing an empirical asymptotic approximation of the mean-integrated-squared-error (MISE) between the underlying density and the estimated GMM density. Lastly, the $\mathcal{K}$-BM, the $\mathcal{K}$-BIC, and the MISE based selection of the kernel's bandwidth are combined into a unified framework for joint order selection and parameter estimation of a GMM. The advantages of the $\mathcal{K}$-divergence based framework over other robust approaches are illustrated in simulation studies involving synthetic and real data.

Non-Gaussianity and non-stationarity are strong features on the basis of which blind source extraction (BSE) becomes a powerful signal processing tool. The recently proposed double nonstationarity model exploits both properties in the mixing and source models, which significantly broadens the class of identifiable signals. In this article, Cramér-Rao and performance analyses are presented, including the complex-valued case, non-circularity, joint extraction, and non-stationary mixing useful for moving source extraction. Besides identifiability conditions and achievable extraction accuracy, the results reveal the influence of a source model misspecification. Of particular interest is the case when the source of interest is Gaussian, which is not identifiable without taking into account source non-stationarity. The validity of the analyses is experimentally confirmed and compared with the empirical performance of the FastDIVA algorithm. It is shown that the closed-form expression obtained from the analysis can be used as information about the achieved interference-to-signal ratio without knowing the ground-truth signals.

Joint blind source separation (JBSS) is a powerful methodology for analyzing multiple related datasets, able to jointly extract sources that describe statistical dependencies across the datasets. However, JBSS can be computationally prohibitive with high-dimensional data, thus there exists a key need for more efficient JBSS algorithms. JBSS algorithms typically rely on numerical solutions, which may be expensive due to their iterative nature. In contrast, analytic solutions follow consistent procedures that are often less expensive. In this paper, we introduce an efficient analytic solution for JBSS. Denoting a set of sources dependent across the datasets as a “source component vector” (SCV), our solution minimizes correlation among separate SCVs by minimizing distance of the SCV cross-covariance’s eigenvector matrix from a block diagonal matrix. Under the orthogonality constraint, this leads to a system of linear equations wherein each subproblem has an analytic solution. We derive identifiability conditions of our solution’s estimator, and demonstrate estimation performance and time efficiency in comparison with other JBSS algorithms that exploit source correlation across datasets. Results demonstrate that our solution achieves the lowest asymptotic computational complexity among JBSS algorithms, and is capable of superior estimation performance compared with algorithms of similar complexity.