Abstract 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 data storage and computing infrastructure. The combination of on‐device AI processing and network connectivity enables to analyse data locally and transmitting only relevant information, greatly reducing the volume of data requiring storage. However, programming these devices for robust operation is challenging, requiring expertise in embedded systems and software engineering. Despite the increase in AI models for bioacoustics, their full potential remains unrealised without accessible tools to deploy and configure them to meet specific monitoring goals. 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 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. 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 several months, deploying two acoupi ‐powered devices in a UK urban park. acoupi can be deployed on low‐cost hardware such as the Raspberry Pi (RPi) and can be customised for various applications. acoupi standardised framework and simplified tools facilitate the adoption of AI‐powered monitoring systems for researchers and conservationists.
Vuilliomenet et al. (Sat,) studied this question.