SPS Newsletter Article

This SPS webinar will introduce a novel data-driven cooperative localization and location data processing framework, called FedLoc, in line with the emerging machine learning and optimization techniques.

Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classification, speech emotion classification, and sound event detection. Recently, neural networks have been applied to tackle audio pattern recognition problems. 

Recently, deep convolutional neural network (CNNs) have been widely used in Single Image Super-Resolution (SISR) and have obtained great success. However, most of the existing methods are limited to local receptive field and equal treatment of different types of information; 

In the beginning of 2020, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, affecting billions of humans. Identifying and separating the infected people during the early stage is the most important step in controlling the pandemic.

In this talk, we will discuss a panoramic view of digital forensics in the last 10 years and how it needed to evolve from basic computer vision and simple natural language processing techniques to powerful AI-driven methods to deal with the signs of the new age. 

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.

Multimedia contents are deeply intertwined with our lives, and, as a consequence, they've become an invaluable asset also for investigative and evidentiary use. However, there are still numerous open challenges for law enforcement agencies when it comes to acquire, authenticate, enhance, and analyze images and videos for forensic use. 

The intent of this webinar is to demonstrate the optimality of splines for the resolution of inverse problems in imaging and the design of deep neural networks. To that end, I first present a representer theorem that states that the extremal points of a broad class of linear inverse problems with a generalized total-variation constraint are adaptive splines whose type is linked to the underlying regularization operator. 

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.

Shrikanth (Shri) Narayanan is University Professor and Niki & C. L. Max Nikias Chair in Engineering at the University of Southern California, where he is Professor of Electrical & Computer Engineering, Computer Science, Linguistics, Psychology, Neuroscience, Pediatrics, and Otolaryngology-Head & Neck Surgery, Director of the Ming Hsieh Institute and Research Director of the Information Sciences Institute.