Generating Diverse Synthetic Morse Code Datasets for Neural-Network Based Classification

This work presents a system for translating between Morse code and human-readable text, with applications in secure and efficient communication. A novel algorithm is proposed to generate one-dimensional synthetic datasets for classifying Morse code using supervised learning, especially neural networks. Despite minimal features, these datasets carry high information density and include added noise to test model robustness. An automated decoder converts Morse signals into audio, offering potential use in secure transmissions by intelligence agencies. The open- source algorithm and datasets encourage further research in classification and communication systems.