Biodiversity monitoring has become a critical task for governments and ecological research agencies for reducing significant loss of animal species. Existing monitoring methods are time-intensive and techniques such as tagging are also invasive and may adversely affect animals. Bioacoustics based monitoring is becoming an increasingly prominent non-invasive method, involving the passive recording of animal sounds. Bioacoustics analysis can provide deep insights into key environmental integrity issues such as biodiversity, density of individuals and present or absence of species. However, analysing environmental recordings is not a trivial task. In last decade several researchers have tried to apply machine learning methods to automatically extract insights from these recordings. To help current researchers and identify research gaps, this paper aims to summarise and classify these works in the form of a taxonomy of the various bioacoustics applications and analysis approaches. We also present a comprehensive survey of bioacoustics data analysis approaches with an emphasis on bird species identification. The survey first identifies common processing steps to analyse bioacoustics data. As bioacoustics monitoring has grown, so does the volume of raw acoustic data that must be processed. Accordingly, this survey examines how bioacoustics analysis techniques can be scaled to work with big data. We conclude with a review of open challenges in the bioacoustics domain, such as multiple species recognition, call interference and automatic selection of detectors.

中文翻译：

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生物声学数据分析——分类、调查和开放挑战

生物多样性监测已成为政府和生态研究机构减少动物物种大量损失的一项关键任务。现有的监测方法需要大量时间，而且标签等技术也是侵入性的，可能会对动物产生不利影响。基于生物声学的监测正成为一种日益突出的非侵入性方法，涉及动物声音的被动记录。生物声学分析可以深入了解关键的环境完整性问题，例如生物多样性、个体密度和物种存在与否。然而，分析环境记录并非易事。在过去十年中，几位研究人员尝试应用机器学习方法从这些录音中自动提取见解。为了帮助当前的研究人员并确定研究差距，本文旨在以各种生物声学应用和分析方法的分类法的形式对这些工作进行总结和分类。我们还对生物声学数据分析方法进行了全面调查，重点是鸟类识别。该调查首先确定了分析生物声学数据的常见处理步骤。随着生物声学监测的发展，必须处理的原始声学数据量也在增加。因此，本次调查研究了如何扩展生物声学分析技术以处理大数据。最后，我们回顾了生物声学领域的开放挑战，例如多物种识别、呼叫干扰和检测器的自动选择。