  This article focuses on signal classification for deep-sea acoustic neutrino
detection. In the deep sea, the background of transient signals is very
diverse. Approaches like matched filtering are not sufficient to distinguish
between neutrino-like signals and other transient signals with similar
signature, which are forming the acoustic background for neutrino detection in
the deep-sea environment. A classification system based on machine learning
algorithms is analysed with the goal to find a robust and effective way to
perform this task. For a well-trained model, a testing error on the level of
one percent is achieved for strong classifiers like Random Forest and Boosting
Trees using the extracted features of the signal as input and utilising dense
clusters of sensors instead of single sensors.
