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The demand for high sound quality is increasing in both entertainment and communications. Consequently, audio restoration algorithms play a critical role in mitigating distortions and interferences that originate from recording processes or arise from imperfect transmission pipelines. This webinar offers an in-depth examination of generative audio restoration algorithms, with a particular focus on diffusion-based techniques for speech enhancement.

Audio source separation is the task of extracting one or more constituent components, or composites thereof, from their mixture. Creatively produced audio signals, such as music and cinematic audio, present a unique challenge for source separation algorithms due to the sheer diversity of potential sound sources within a particular mixture. However, most state-of-the-art deep learning systems for source separation have often been either a collection of single-source separators or a tightly coupled system that cannot be easily adapted to support additional or unseen sound sources.

In this talk, the presenter will discuss the progress made over the last decade for the implementation of clinical AI solutions for digital pathology. She will draw upon both academic and industrial experiences to discuss the various stages of growth of AI for digital pathology, including the challenges of the past, where we are now, and some perspectives for the future.

Deep Neural Networks (DNNs) have had tremendous positive impact on performance in several tasks in image processing (e.g. image segmentation). In this seminar, I will present works done in my team for improving the performance of DNNs, and how efficient solutions based on DNN combined with Graphs, can be designed in applications such as automatic discovery and geotagging of objects.

In the rapidly evolving field of computational imaging, the coherent codesign of image sensors and signal/image processing unlocks numerous new capabilities. To retrieve sparse macroscopic impulse response functions (IRF) to modulated light we integrate advanced circuit design with signal processing techniques. We introduce a novel sensor design that enables the realization of a single-shot Fourier camera, alongside computationally efficient methods for sparse IRF recovery.

Graph Fourier transform (GFT) is one of the fundamental tools in graph signal processing to decompose graph signals into different frequency components and effectively represent graph signals with strong correlations using various modes of variation. The GFT on undirected graphs has been well-studied, with a conventional approach based on the eigen decomposition of the graph Laplacian. However, this method does not apply to directed graph settings.

In wideband integrated sensing and communications (ISAC) systems, conventional designs optimize dual-functional waveforms over the entire bandwidth. This approach significantly restricts beamforming flexibility and degrades communications performance under strict radar sensing constraints. This webinar presents a novel subcarrier allocation strategy to enhance the communications–sensing performance tradeoff in ISAC. The proposed method selectively assigns a subset of subcarriers for radar sensing while enabling communication across the full bandwidth.

Deep Learning (DL) methods currently obtain state-of-the-art performance in the tasks of positional Sound Source Localization (SSL) and acoustic Direction of Arrival (DOA) estimation. However, most DL methods require matched microphone array geometries between training and testing scenarios, requiring separate models to be trained for different devices. In this webinar, the presenter will present geometry-aware and geometry-agnostic DL approaches for SSL, comparing their advantages and drawbacks, and future research directions.

This webinar session will explore personalized federated learning (PFL), a cutting-edge method that creates customized models for diverse client needs, enhancing model convergence through meta-learning. We'll address the significant challenges associated with information leakage and unveil a differential privacy (DP) based PFL framework. Our approach designs a privacy budget allocation scheme rooted in the Rényi Differential Privacy composition theory. Throughout the session, we will discuss the formulation of convergence bounds applicable to both convex and non-convex loss functions.

Currently, the rapid advancement of generative artificial intelligence, particularly diffusion models and large language models, has enabled the creation or manipulation of high-quality multimedia. Although beneficial in numerous fields, AI generated content presents significant risks, including potential misuse and ethical concerns. Consequently, detecting the generated multimedia has become increasingly popular in related research. This webinar presents a comprehensive overview of recent deep learning-based approaches for AI generated content detection.