Multimodal Signal

Multimodal signal processing focuses on integrating information from diverse sources, such as audio, video, text, and physiological data, to achieve more robust and comprehensive analyses than using any single modality alone. Current research emphasizes developing models, including transformers, diffusion models, and neural networks like LSTMs, to effectively fuse these heterogeneous data types for tasks ranging from emotion recognition and human-robot interaction to medical image synthesis and automated driving. This field is significant because it enables more accurate and nuanced understanding of complex systems and behaviors, leading to advancements in various applications including healthcare, robotics, and autonomous systems.