Automatic detection and classification of animal sounds has many applications in biodiversity monitoring and animal behaviour. In the past twenty years, the volume of digitised wildlife sound available has massively increased, and automatic classification through deep learning now shows strong results. However, bioacoustics is not a single task but a vast range of small-scale tasks (such as individual ID, call type, emotional indication) with wide variety in data characteristics, and most bioacoustic tasks do not come with strongly-labelled training data. The standard paradigm of supervised learning, focussed on a single large-scale dataset and/or a generic pre-trained algorithm, is insufficient. In this work we recast bioacoustic sound event detection within the AI framework of few-shot learning. We adapt this framework to sound event detection, such that a system can be given the annotated start/end times of as few as 5 events, and can then detect events in long-duration audio -- even when the sound category was not known at the time of algorithm training. We introduce a collection of open datasets designed to strongly test a system's ability to perform few-shot sound event detections, and we present the results of a public contest to address the task. We show that prototypical networks are a strong-performing method, when enhanced with adaptations for general characteristics of animal sounds. We demonstrate that widely-varying sound event durations are an important factor in performance, as well as non-stationarity, i.e. gradual changes in conditions throughout the duration of a recording. For fine-grained bioacoustic recognition tasks without massive annotated training data, our results demonstrate that few-shot sound event detection is a powerful new method, strongly outperforming traditional signal-processing detection methods in the fully automated scenario.

翻译：自动检测与分类动物声音在生物多样性监测和动物行为研究中有广泛应用。过去二十年中，数字化野生动物声音数据量大幅增长，基于深度学习的自动分类方法已展现出显著成效。然而，生物声学并非单一任务，而是涵盖个体识别、叫声类型、情绪指示等大量细粒度任务，其数据特征差异巨大，且多数生物声学任务缺乏强标注训练数据。以单一大规模数据集和/或通用预训练算法为核心的标准监督学习范式难以满足需求。本研究将生物声学声音事件检测重构为人工智能领域中的小样本学习框架。通过将这一框架适配至声音事件检测任务，系统仅需基于少至5个事件的带标注起始/结束时间，即可在长时段音频中检测事件——即使该声音类别在算法训练时未知。我们引入了一套公开数据集组合，旨在严格测试系统执行小样本声音事件检测的能力，并展示了针对该任务的公开竞赛结果。研究表明，当针对动物声音的通用特性进行适应性增强后，原型网络是一种高性能方法。我们证实：广泛变化的声音事件时长以及非平稳性（即录音过程中条件的渐进变化）是影响性能的关键因素。针对缺乏大规模标注训练数据的细粒度生物声学识别任务，本研究表明小样本声音事件检测是一种强大的新方法，在全自动场景下显著优于传统信号处理检测方法。