Signal Decomposition

Signal decomposition aims to separate a complex signal into simpler, meaningful components, facilitating analysis and interpretation across diverse fields. Current research emphasizes developing robust and efficient decomposition methods, particularly focusing on deep learning architectures like convolutional neural networks and recurrent neural networks, as well as leveraging techniques like compressed sensing and sparse representations. These advancements are improving accuracy and efficiency in applications ranging from time series prediction and audio source separation to image generation and terrain reconstruction, addressing challenges like mode mixing and boundary effects in existing methods. The resulting improvements in signal processing have significant implications for various scientific disciplines and technological applications.