Ecological and conservation studies monitoring bird communities typically rely on species classification based on bird vocalizations. Historically, this has been based on expert volunteers going into the field and making lists of the bird species that they observe. Recently, machine learning algorithms have emerged that can accurately classify bird species based on audio recordings of their vocalizations. Such algorithms crucially rely on training data that are labeled by experts. Automated classification is challenging when multiple species are vocalizing simultaneously, there is background noise, and/or the bird is far from the microphone. In continuously monitoring different locations, the size of the audio data become immense and it is only possible for human experts to label a tiny proportion of the available data. In addition, experts can vary in their accuracy and breadth of knowledge about different species. This article focuses on the important problem of combining sparse expert annotations to improve bird species classification while providing uncertainty quantification. We additionally are interested in providing expert performance scores to increase their engagement and encourage improvements. We propose a Bayesian hierarchical modeling approach and evaluate this approach on a new community science platform developed in Finland.

翻译：生态学与保护研究中监测鸟类群落通常依赖基于鸟类鸣声的物种分类。传统上，这依赖专家志愿者前往野外并记录观察到的鸟类物种清单。近年来，基于音频录音的机器学习算法已能准确分类鸟类物种。这类算法关键依赖于由专家标注的训练数据。当多个物种同时发声、存在背景噪声和/或鸟类远离麦克风时，自动化分类面临挑战。在连续监测不同地点的过程中，音频数据规模变得极其庞大，人类专家仅能标注其中极小比例的数据。此外，不同专家对不同物种的识别准确率和知识广度存在差异。本文聚焦于结合稀疏专家标注以改进鸟类物种分类同时提供不确定性量化这一重要问题。我们还旨在提供专家性能评分以提升其参与度并鼓励改进。我们提出一种贝叶斯层次建模方法，并在芬兰开发的新社区科学平台上对该方法进行验证。