Signal to Noise Ratio

Signal-to-noise ratio (SNR) quantifies the strength of a signal relative to background noise, a crucial factor in various fields aiming to extract meaningful information from noisy data. Current research focuses on improving SNR in diverse applications, employing techniques like end-to-end learning for optimized signal processing, deep learning models for noise reduction and feature extraction (including convolutional neural networks and diffusion models), and adversarial learning to enhance robustness against domain shifts. These advancements have significant implications for improving the accuracy and reliability of diverse technologies, ranging from medical imaging and radar systems to speech recognition and wireless communications.