  Few-shot learning is a type of classification through which predictions are
made based on a limited number of samples for each class. This type of
classification is sometimes referred to as a meta-learning problem, in which
the model learns how to learn to identify rare cases. We seek to extract
information from five exemplar vocalisations of mammals or birds and detect and
classify these sounds in field recordings [2]. This task was provided in the
Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge of
2021. Rather than utilize deep learning, as is most commonly done, we
formulated a novel solution using only machine learning methods. Various models
were tested, and it was found that logistic regression outperformed both linear
regression and template matching. However, all of these methods over-predicted
the number of events in the field recordings.
