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Section: Publications & Resources

The Audio Engineering Society (AES), the IEEE Consumer Technology Society (CTSoc), and the IEEE Signal Processing Society (SPS) cordially invite you to a first-of-a-kind joint event discussing the state of the art perspectives in this rapidly evolving field.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

The July issue of IEEE Signal Processing Magazine (SPM) is a special issue focused on “Explainability in Data Science: Interpretability, Reproducibility, and Replicability.” With increased enthusiasm for machine learning, it is a very timely topic, and I invite every IEEE Signal Processing Society (SPS) member to read these very instructive papers.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

Most of the work we do in signal processing these days is data driven. The shift from the more traditional and model-driven approaches to those that are data driven has also underlined the importance of explainability of our solutions. Because most traditional signal processing approaches start with a number of modeling assumptions, they are comprehensible by the very nature of their construction.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

Data-driven solutions are playing an increasingly important role in numerous practical problems across multiple disciplines. The shift from the traditional model-driven approaches to those that are data driven naturally emphasizes the importance of the explainability of solutions, as, in this case, the connection to a physical model is often not obvious. Explainability is a broad umbrella and includes interpretability, but it also implies that the solutions need to be complete, in that one should be able to “audit” them, ask appropriate questions, and hence gain further insight about their inner workings.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

In many modern data science problems, data are represented by a graph (network), e.g., social, biological, and communication networks. Over the past decade, numerous signal processing and machine learning (ML) algorithms have been introduced for analyzing graph structured data. With the growth of interest in graphs and graph-based learning tasks in a variety of applications, there is a need to explore explainability in graph data science.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex nonlinear learning models, such as deep neural networks. Gaining a better understanding is especially important, e.g., for safety-critical ML applications or medical diagnostics and so on. Although such explainable artificial intelligence (XAI) techniques have reached significant popularity for classifiers, thus far, little attention has been devoted to XAI for regression models (XAIR). 

Section: Events

July 11-15, 2022 Registration Deadline: July 5, 2022 Location: Virtual

Section: Events

Date: 5-6 June 2023 Location: Santa Clara, CA, USA

LECTURER/SENIOR LECTURER IN AUDIO ENGINEERING (RESEARCH AND TEACHING) University of Surrey Sound & Video Recording Location: Guildford Salary: £42,149 to £61,819 per annum Post Type: Full Time, Permanent Closing Date: 23.59 hours BST on Wednesday 20 July 2022 Reference: 042322   We seek an outstanding, self-motivated Lecturer to contribute to the education of exceptional future audio engineers and musicians on the BMus/BSc Music and Sound Recording (Tonmeister®) programme in the Department of Music and Sound Recording, and to conduct world-leading research.   The succe