Tutorial: Generative AI Models for Signal and Data Processing: Theory, Methods, and Applications (Part 2 of 2)

Generative AI models have emerged as a groundbreaking paradigm that can generate, modify, and interpret complex data patterns, ranging from images and sounds to structured datasets. In the realm of signal processing, these models have the potential to revolutionize how we understand, process, and leverage signals. Their capabilities span from the generation of synthetic datasets to the enhancement and restoration of signals, often achieving results that traditional methods can't match. Thus, understanding and harnessing the power of generative AI is not just an academic endeavor; it's becoming an imperative for professionals and researchers who aim to stay at the forefront of the signal processing domain. The last few years have witnessed an explosive growth in the development and adoption of generative AI models. With the introduction of architectures like GANs, VAEs, and newer transformer-based models, the AI research community is regularly setting new performance benchmarks. The signal processing community also begins to exploit these advancements. The year 2024 presents a crucial juncture where the convergence of AI and signal processing is no longer a future possibility but an ongoing reality. Thus, a tutorial on this topic is not just timely but urgently needed. While there have been numerous tutorials and courses on generative AI in the context of computer vision or natural language processing, its application in the pure signal and data processing domain is less explored. This tutorial is unique in its comprehensive approach, combining theory, practical methods, and a range of applications specifically tailored for the signal processing community. Attendees will not only learn about the core concepts but will also gain theory and application of generative AI techniques. Generative AI provides a fresh lens through which to approach longstanding challenges in signal processing. This tutorial will introduce: New Ideas: Concepts like latent space exploration, variational inference, and diffusion models which can provide new insights into signal representation and transformation. New Topics: Areas where generative AI has found success, such as data augmentation, signal enhancement, and anomaly detection in signals. New Tools: Practical demonstrations and hands-on sessions using state-of-the-art software libraries and tools tailored for generative AI in signal processing. In conclusion, by bridging the gap between the advancements in generative AI and the vast potential applications in signal processing, this tutorial promises to equip attendees with knowledge and tools that can redefine the boundaries of what's possible in the field.