Bioacoustic Event Detection with prototypical networks and data augmentation

Understand

This report presents deep learning and data augmentation techniques used by a system entered into the Few-Shot Bioacoustic Event Detection for the DCASE2021 Challenge.

• The remit was to develop a few-shot learning system for animal (mammal and bird) vocalisations.
• Participants were tasked with developing a method that can extract information from five exemplar vocalisations, or shots, of mammals or birds and detect and classify sounds in field recordings.
• In the system described in this report, prototypical networks are used to learn a metric space, from which classification is performed by computing the distance of a query point to class prototypes, classifying based on shortest distance.