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

Luxi Huang; Shaohua Shi; Yingming Chen; Ping Feng; Xiaohui Li

doi:10.1117/12.3053586

11 December 2024 Advancing signal separation: a study of machine learning in low-frequency time code multipath signals

Luxi Huang, Shaohua Shi, Yingming Chen, Ping Feng, Xiaohui Li

Author Affiliations +

Proceedings Volume 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024); 1344502 (2024) https://doi.org/10.1117/12.3053586
Event: International Conference on Electronics. Electrical and Information Engineering (ICEEIE 2024), 2024, Haikou, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Luxi Huang, Shaohua Shi, Yingming Chen, Ping Feng, and Xiaohui Li "Advancing signal separation: a study of machine learning in low-frequency time code multipath signals", Proc. SPIE 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024), 1344502 (11 December 2024); https://doi.org/10.1117/12.3053586

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