Abstract
This study presents a comprehensive overview of the classification of time signals over a variety of objects. Signals were initially processed using the Hilbert-Huang transform, followed by supervised machine learning and deep learning to classify objects. Multilayer Perceptron (MLP) and Support Vector Machines (SVM) were used for sound discrimination. The result is a program that effectively detects and classifies time signals as “Object 1” or “Not Object 1” (i.e., Object #2 and Object 3).
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Jadoon, I.A., Logofătu, D., Islam, M.N. (2023). Classification of Time Signals Using Machine Learning Techniques. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_8
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