  The aim of atypicality is to extract small, rare, unusual and interesting
pieces out of big data. This complements statistics about typical data to give
insight into data. In order to find such "interesting" parts of data, universal
approaches are required, since it is not known in advance what we are looking
for. We therefore base the atypicality criterion on codelength. In a prior
paper we developed the methodology for discrete-valued data, and the the
current paper extends this to real-valued data. This is done by using minimum
description length (MDL). We show that this shares a number of theoretical
properties with the discrete-valued case. We develop the methodology for a
number of "universal" signal processing models, and finally apply them to
recorded hydrophone data.
