开源工具acoupi让研究人员轻松在边缘设备上部署生物声学AI模型。
acoupi: An Open-Source Python Framework for Deploying Bioacoustic AI Models on Edge Devices
- 基于Python的模块化框架，整合录音、智能处理与无线传输
- 在城市公园连续运行一个月，支持BirdNET和BatDetect2模型
- 适配树莓派等低成本硬件，助力科研与保护工作
被动式声学监测（PAM）结合人工智能已成为生物多样性监测的重要手段。传统PAM系统需人工数据导出，对存储与算力要求高。通过在设备端进行AI处理并仅传输关键信息，可显著降低存储需求。然而，实现稳定运行需嵌入式系统与软件工程经验，门槛较高。尽管已有多种生物声学AI模型，但缺乏易用工具将其部署于定制硬件并适配具体监测目标。为此，我们开发了acoupi——一个开源Python框架，简化智能生物声学设备的创建与部署。acoupi将音频录制、AI处理、数据管理与实时无线通信整合为统一可配置框架，通过模块化设计，使用户能灵活定制或替换组件以满足特定需求。我们通过集成BirdNET（鸟类分类）与BatDetect2（英国蝙蝠分类）模型，在英国城市公园完成了为期一个月的双设备部署测试，验证了其可靠性。acoupi支持树莓派等低成本硬件，标准化流程与简化工具极大促进了AI驱动的PAM系统在科研与保护领域的应用。项目已开源，地址：https://github.com/acoupi/acoupi。
原文摘要 · Abstract (English)
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.
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