Classification of Bioacoustic Time Series by Training a Fusion Layer with Decision Templates

The classification of time series is the topic of this paper. In particular we discuss the combination of local classifier decisions from several feature spaces with static and adaptable fusion schemes, e.g. averaging and decision templates. The decision templates are calculated over a set of feature vectors which are extracted in local time windows. We present algorithms to calculate decision templates, and demonstrate the behaviour of this approach on a real world data set from the field of bioacoustics.