Advancing signal separation: a study of machine learning in low-frequency time code multipath signals

The goal of this study is to apply machine learning methods in low-frequency time code multi-path signal separation. Through a comparative analysis, traditional signal separation techniques like adaptive filtering and Independent Component Analysis (ICA) are weighed against three notable machine learning algorithms: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM). Through simulation experiments, the feasibility of machine learning in low-frequency time code signal separation was verified. The results affirm the superiority of machine learning algorithms, with LSTM notably showcasing the highest output signal-to-noise ratio and minimized mean squared error. Additionally, CNN excels in scenarios with smaller signal delays and suboptimal signal-to-noise ratios, while RNN performs between CNN and LSTM. Furthermore, for similar one- dimensional signal separation problems, machine learning, especially LSTM, is also a good solution.