Signal Classification

Signal classification aims to automatically categorize signals based on their inherent characteristics, a crucial task across diverse fields like wireless communications, medical diagnostics, and astrophysics. Current research emphasizes improving the robustness and efficiency of classification models, focusing on deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, often incorporating techniques like model pruning, knowledge distillation, and ensemble methods to enhance performance and reduce computational demands. These advancements have significant implications for various applications, enabling more accurate and efficient signal processing in areas ranging from spectrum management and anomaly detection to medical imaging and gravitational wave analysis.