24th EANN 2023, 14 - 17 June 2023, León, Spain

Classification of Time Signals Using Machine Learning Techniques

Ishfaq Ahmad Jadoon, Doina Logofatu, Mohammad Nahin Islam


  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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