acoupi：一种用于在边缘设备上部署生物声学AI模型的开源Python框架
acoupi: An Open-Source Python Framework for Deploying Bioacoustic AI Models on Edge Devices
AI总结：
acoupi是一个开源Python框架，旨在简化在边缘设备上部署生物声学AI模型，通过模块化设计提升生物多样性监测的灵活性和可定制性。
摘要：
被动声学监测（PAM）结合人工智能（AI）正成为生物多样性监测的重要工具。传统PAM系统需要手动数据下传，并对存储和计算基础设施提出巨大需求。将设备上的AI处理与网络连接相结合，可以实现本地数据分析和仅传输相关信息，大大减少存储需求。然而，编程这些设备以实现稳健运行具有挑战性，需要嵌入式系统和软件工程方面的专业知识。尽管生物声学领域已出现越来越多基于AI的模型，但如果没有可用于在定制硬件上部署它们的工具，其全部潜力仍未得到实现。为了解决这一挑战，我们开发了acoupi，一种开源的Python框架，它简化了智能生物声学设备的创建和部署。acoupi将音频录制、基于AI的数据处理、数据管理和实时无线消息传递整合到一个统一且可配置的框架中。通过模块化生物声学监测工作流程的关键元素，acoupi允许用户轻松地自定义、扩展或选择特定组件以满足其独特的监测需求。我们通过整合两个生物声学分类器：BirdNET用于鸟类物种分类，BatDetect2用于英国蝙蝠物种分类，展示了acoupi的灵活性。我们测试了acoupi在英国城市公园两个月的部署中的一致性。acoupi可以在低成本硬件如Raspberry Pi上部署，并可根据各种应用进行定制。acoupi标准化的框架和简化工具促进了AI驱动的PAM系统在研究人员和保护主义者中的采用。acoupi在GitHub上可获得：https://github.com/acoupi/acoupi。
英文摘要：
1. Passive acoustic monitoring (PAM) coupled with artificial intelligence (AI) is becoming an essential tool for biodiversity monitoring. Traditional PAM systems require manual data offloading and impose substantial demands on storage and computing infrastructure. The combination of on-device AI-based processing and network connectivity enables local data analysis and transmission of only relevant information, greatly reducing storage needs. However, programming these devices for robust operation is challenging, requiring expertise in embedded systems and software engineering. Despite the increase in AI-based models for bioacoustics, their full potential remains unrealized without accessible tools to deploy them on custom hardware and tailor device behaviour to specific monitoring goals. 2. To address this challenge, we develop acoupi, an open-source Python framework that simplifies the creation and deployment of smart bioacoustic devices. acoupi integrates audio recording, AI-based data processing, data management, and real-time wireless messaging into a unified and configurable framework. By modularising key elements of the bioacoustic monitoring workflow, acoupi allows users to easily customise, extend, or select specific components to fit their unique monitoring needs. 3. We demonstrate the flexibility of acoupi by integrating two bioacoustic classifiers: BirdNET, for the classification of bird species, and BatDetect2, for the classification of UK bat species. We test the reliability of acoupi over a month-long deployment of two acoupi-powered devices in a UK urban park. 4. acoupi can be deployed on low-cost hardware such as the Raspberry Pi and can be customised for various applications. acoupi standardised framework and simplified tools facilitate the adoption of AI-powered PAM systems for researchers and conservationists. acoupi is on GitHub at https://github.com/acoupi/acoupi.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
