Decision templates for the classification of bioacoustic time series

Abstract

The classification of time series is topic of this paper. In particular we discuss the combination of multiple classifier outputs with decision templates. The decision templates are calculated over a set of feature vectors which are extracted in local time windows. To learn characteristic classifier outputs of time series a set of decision templates is determined for the individual classes. We present algorithms to calculate multiple decision templates, and demonstrate the behaviour of this new approach on a real world data set from the field of bioacoustics.