  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.
