Signal Separation

Signal separation aims to isolate individual signals from a composite mixture, a crucial task across diverse fields. Current research heavily utilizes deep learning, employing architectures like autoencoders, recurrent neural networks (RNNs, particularly Bi-LSTMs), and WaveNet adaptations, often combined with techniques like compressed sensing to improve efficiency and robustness. These methods address challenges in various domains, including removing noise from EEG and RF signals, separating signals in underwater acoustics, and performing semantic segmentation of point clouds, demonstrating significant improvements over traditional approaches. The resulting advancements have broad implications for applications ranging from medical diagnostics to communication technologies.