  Deep learning has been widely used recently for sound event detection and
classification. Its success is linked to the availability of sufficiently large
datasets, possibly with corresponding annotations when supervised learning is
considered. In bioacoustic applications, most tasks come with few labelled
training data, because annotating long recordings is time consuming and costly.
Therefore supervised learning is not the best suited approach to solve
bioacoustic tasks. The bioacoustic community recasted the problem of sound
event detection within the framework of few-shot learning, i.e. training a
system with only few labeled examples. The few-shot bioacoustic sound event
detection task in the DCASE challenge focuses on detecting events in long audio
recordings given only five annotated examples for each class of interest. In
this paper, we show that learning a rich feature extractor from scratch can be
achieved by leveraging data augmentation using a supervised contrastive
learning framework. We highlight the ability of this framework to transfer well
for five-shot event detection on previously unseen classes in the training
data. We obtain an F-score of 63.46\% on the validation set and 42.7\% on the
test set, ranking second in the DCASE challenge. We provide an ablation study
for the critical choices of data augmentation techniques as well as for the
learning strategy applied on the training set.
