Few-shot bioacoustic event detection: A new task at the DCASE 2021 challenge

Few-shot bioacoustic event detection is a novel area of research that emerged from a need in monitoring biodiversity and animal behaviour: to annotate long recordings, that experts usually can only provide very few annotations for due to the task being specialist and labour-intensive. This paper presents an overview of the first evaluation of few-shot bioacoustic sound event detection, organised as a task of the DCASE 2021 Challenge. A set of datasets consisting of mammal and bird multi-species recordings in the wild, along with class-specific temporal annotations, was compiled for the challenge, for the purpose of training learning-based approaches and for evaluation of the submissions in a few-shot labelled dataset. This paper describes the task in detail, the datasets that were used for both development and evaluation of the submitted systems, along with how system performance was ranked and the characteristics of the best-performing submissions. Some common strategies that the participating teams used are discussed, including input features, model architectures, transferring of prior knowledge, use of public datasets and data augmentation. Ranking for the challenge was based on overall performance of the evaluation set, however in this paper we also present results on each of the subsets of the evaluation set. This new analysis reveals submissions that performed better on specific subsets and gives an insight as to characteristics of the subsets that can influence performance