Smooth Signal

Smooth signal processing focuses on analyzing and reconstructing signals exhibiting smoothness properties, often defined over graph structures. Current research emphasizes developing efficient online algorithms, such as proximal ADMM and variations of least mean pth power methods, for handling streaming data and non-Gaussian noise, as well as employing neural networks and graph signal processing tools for improved signal representation and classification. These advancements are crucial for applications like emotion recognition from EEG data, graph inference from noisy observations, and efficient signal interpolation in audio processing, ultimately improving the accuracy and robustness of signal analysis across diverse fields.