Few-shot bioacoustic event detection consists in detecting sound events of specified types, in varying soundscapes, while having access to only a few examples of the class of interest. This task ran as part of the DCASE challenge for the third time this year with an evaluation set expanded to include new animal species, and a new rule: ensemble models were no longer allowed. The 2023 few shot task received submissions from 6 different teams with F-scores reaching as high as 63% on the evaluation set. Here we describe the task, focusing on describing the elements that differed from previous years. We also take a look back at past editions to describe how the task has evolved. Not only have the F-score results steadily improved (40% to 60% to 63%), but the type of systems proposed have also become more complex. Sound event detection systems are no longer simple variations of the baselines provided: multiple few-shot learning methodologies are still strong contenders for the task.

翻译：少样本生物声学事件检测任务旨在从不同声景中检测指定类型的声学事件，且仅能获取目标类别的少量样本。该任务今年第三次作为DCASE挑战赛的组成部分开展，其评估集已扩展至包含新动物物种，并新增一项规则：不再允许使用集成模型。2023年少样本任务共收到来自6个参赛团队的方案，在评估集上F分数最高达到63%。本文对该任务进行描述，重点阐述与往年不同的要素。同时回顾往届赛事，梳理任务演变历程。不仅F分数结果持续提升（从40%到60%再到63%），所提出的系统类型也日趋复杂。声学事件检测系统已不再是对基线方案的简单变体：多种少样本学习方法在该任务中仍具有强大竞争力。