  This work introduces the one-shot learning paradigm in the computational
bioacoustics domain. Even though, most of the related literature assumes
availability of data characterizing the entire class dictionary of the problem
at hand, that is rarely true as a habitat's species composition is only known
up to a certain extent. Thus, the problem needs to be addressed by
methodologies able to cope with non-stationarity. To this end, we propose a
framework able to detect changes in the class dictionary and incorporate new
classes on the fly. We design an one-shot learning architecture composed of a
Siamese Neural Network operating in the logMel spectrogram space. We
extensively examine the proposed approach on two datasets of various bird
species using suitable figures of merit. Interestingly, such a learning scheme
exhibits state of the art performance, while taking into account extreme
non-stationarity cases.
