  Underwater acoustic monitoring systems record many hours of audio data for
marine research, making fast and reliable non-causal signal detection
paramount. Such detectors assist in reducing the amount of labor required for
signal annotations, which often contain large portions devoid of signals.
  Cetacean vocalization detection based on spectral entropy is investigated as
a means of vocalization discovery. Previous techniques using spectral entropy
(SE) mostly consider time-frequency enhancement of the entropy measure, and
utilize the STFT as its time-frequency (TF) decomposition. SE methods also
requires the user to set a detection threshold manually, which call for
knowledge of the produced entropy measures.
  This paper considers median filtering as a simple, effective way to provide
temporal stabilization to the entropy measure, and considers the CWT as an
alternative TF decomposition. K-means clustering is used to determine the
threshold required to accurately separate the signal/no-signal entropy
measures, resulting in a one-dimensional, two-class classification problem. The
class means are used to perform pseudo-probabilistic soft class assignment,
which is a useful metric in algorithmic development. The effect of median
filtering, signal-to-noise ratio and the chosen TF decomposition are
investigated.
  The proposed method shows a significant improvement in detection accuracy and
specificity, while also providing a more interpretable detection threshold
setting via soft class assignment.
