2020/04/14 by R. V. Ramos, Ramos, R. V.
Computer Science · Engineering · #Data Analysis #FOS: Physical sciences #Fault Detection and Control Systems #Neural Networks and Applications #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2005.01602
openalex publication_date 2020/04/14 · openalex created_date 2022/11/29 · openalex updated_date 2026/07/28
The amount of randomness in a signal generated by physical or non-physical process can reveal important information about that process. For example, the presence of randomness in ECG signals may indicate a cardiac disease. On the hand, the lack of randomness in a speech signal may indicate the speaker is a machine. Hence, to quantify the amount of randomness in a signal is an important task in many different areas. In this direction, the present work proposes to use the disentropy of the autocorrelation function as a measure of randomness. Examples using noisy and chaotic signals are shown.