1. Passive acoustic monitoring is increasingly being applied to terrestrial, marine and freshwater environments, providing cost-efficient methods for surveying biodiversity. However, processing the avalanche of audio recordings remains challenging, and represents nowadays a major bottleneck that slows down its application in research and conservation.
2. We present scikit-maad, an open-source Python package dedicated to the analysis of environmental audio recordings. This package was designed to (a) load and process digital audio, (b) segment and find regions of interest, (c) compute acoustic features and (d) estimate sound pressure levels. The package also provides field recordings and a comprehensive online documentation that includes practical examples with step-by-step instructions for beginners and advanced users.
3. scikit-maad opens the possibility to efficiently scan large audio datasets and easily integrate additional machine learning Python packages into the analysis, allowing to measure acoustic properties and identify key patterns in all kinds of soundscapes. To support reproducible research, the package is released under the BSD open-source licence, which allows unrestricted redistribution for commercial and private use.
4. This development will create synergies between the community of ecoacousticians, such as engineers, data scientists, ecologists, biologists and conservation practitioners, to explore and understand the processes underlying the acoustic diversity of ecological systems.

中文翻译：

---

scikit-maad：一个开源的模块化工具箱，用于 Python 中的定量音景分析

1. 被动声学监测越来越多地应用于陆地、海洋和淡水环境，为调查生物多样性提供了具有成本效益的方法。然而，处理大量录音仍然具有挑战性，并且是当今减缓其在研究和保护中应用的主要瓶颈。
2. 我们介绍了 scikit-maad，这是一个开源 Python 包，专门用于分析环境录音。该软件包旨在（a）加载和处理数字音频，（b）分割和查找感兴趣的区域，（c）计算声学特征和（d）估计声压级。该软件包还提供了现场录音和全面的在线文档，其中包括为初学者和高级用户提供分步说明的实际示例。
3. scikit-maad 开启了高效扫描大型音频数据集并轻松将额外的机器学习 Python 包集成到分析中的可能性，允许测量声学特性并识别各种音景中的关键模式。为了支持可重复的研究，该软件包在 BSD 开源许可证下发布，允许无限制地重新分发用于商业和私人用途。
4. 这一发展将在工程师、数据科学家、生态学家、生物学家和保护从业者等生态声学家社区之间产生协同作用，以探索和理解生态系统声学多样性背后的过程。