The spectral entropy (SE) of a signal is a measure of its spectral power distribution. The concept is based on the Shannon entropy, or information entropy, in information theory. The SE treats the signal's normalized power distribution in the frequency domain as a probability distribution, and calculates the Shannon entropy of it.
In general, Shannons' entropy of a signal is in between 0 and 1. 0 means no information content is present in the signal and 1 means otherwise. In communications, if I recall correctly we are uncertain of what the next symbol should be and so we prefer uncertainty as it conveys more information. My question is what if the entropy of the signal (spectral entropy) is say 0.12 i.e., quite small. For example, in a communication channel there is white noise but as soon as somebody speaks, the spectral entropy decreases (please see the example of detecting sine wave from white noise https://www.mathworks.com/help/signal/ref/pentropy.html)
Therefore, the information content of the signal now reduces during the time interval of the duration of the speech signal.
QUESTION: Does the reduced information content of the signal imply that the speech signal contains no information? How come a speech signal has no information or worth? Do we prefer high or low entropy (spectral and Shannon entropy)?
My confusions are:
High entropy --> more information content -- is that preferred or is useless?
Low entropy --> less information content -- Is that preferred?
Please correct me where wrong.