1. Acoustic indices combined with clustering and classification approaches have been increasingly used to automate identification of the presence of vocalizing taxa or acoustic events of interest. While most studies using this approach standardize data collection and study design parameters at the project or study level, recent trends in ecological research are to investigate patterns at regional or continental scales. Large‐scale studies often require collaboration between research groups and integration of data from multiple sources to fulfil objectives, which can lead to variation in recording equipment and data collection protocols.
2. Our objectives were to determine how analytical approaches and variation in data collection and processing that is typical of regional acoustic monitoring programmes influences accuracy when identifying vocal activity in breeding birds. We used data from three regional datasets in Northern Alberta, Northern British Columbia, and Southern and Central Yukon, Canada to investigate the effect of analytical framework, sample size, local species richness and data collection variables on classification accuracy.
3. We found supervised classification approaches to be the most effective, with boosted regression trees identifying vocalizations of breeding birds in audio recordings with a 92.0% accuracy and easily able to accommodate variation in data collection and processing parameters. We also provide recommendations on effectively processing large and heterogeneous datasets including sufficient sample size, accommodating potentially confounding variables and selecting suitable model training data.
4. The results presented in this study can help inform decisions in data collection, data processing, and study design and analysis, maximize performance and accuracy during analysis, and efficiently process large, heterogeneous acoustic datasets to answer questions at scales previously difficult to investigate.

中文翻译：

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使用区域和异构数据集中的声学索引自动分类鸟类的声音活动

1. 结合使用聚类和分类方法的声学索引已越来越多地用于自动识别发声分类单元或感兴趣的声学事件的存在。虽然大多数使用这种方法的研究在项目或研究级别上标准化了数据收集和研究设计参数，但生态研究的最新趋势是研究区域或大陆规模的模式。大型研究通常需要研究小组之间的协作以及来自多个来源的数据集成来实现目标，这可能导致记录设备和数据收集协议的变化。
2. 我们的目标是确定在识别繁殖鸟类的声音活动时，区域性声音监测程序的典型分析方法和数据收集与处理中的变化如何影响准确性。我们使用来自北艾伯塔省，北不列颠哥伦比亚省以及加拿大育空地区南部和北部的三个区域数据集的数据来研究分析框架，样本量，本地物种丰富度和数据收集变量对分类准确性的影响。
3. 我们发现监督分类方法是最有效的，增强的回归树可以在音频记录中识别出繁殖鸟的发声，准确度达到92.0％，并且能够轻松适应数据收集和处理参数的变化。我们还提供有关有效处理大型异构数据集的建议，包括足够的样本量，容纳潜在混淆的变量以及选择合适的模型训练数据。
4. 这项研究中提出的结果可以帮助指导数据收集，数据处理以及研究设计和分析中的决策，在分析过程中最大限度地提高性能和准确性，并有效地处理大型的异构声学数据集，以以前难以调查的规模回答问题。