Signal to Signal Translation

Signal-to-signal translation (Sig2Sig) focuses on converting one type of signal into another, aiming to improve signal quality, extract relevant information, or enable new applications. Current research utilizes various deep learning architectures, including conditional generative adversarial networks (cGANs) and convolutional neural networks (CNNs), often incorporating techniques like optimal transport for data association and iterative refinement for improved efficiency. These advancements have implications across diverse fields, from enhancing speech processing and audio source separation to improving self-positioning systems and enabling novel approaches to motion correction in medical imaging.