acoupi: An Open-Source Python Framework for Deploying Bioacoustic AI Models on Edge Devices (2501.17841v1)

Abstract: 1. Passive acoustic monitoring (PAM) coupled with 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.

• The paper introduces acoupi as an open-source Python framework to deploy bioacoustic AI models on cost-effective edge devices.
• It integrates modular components for audio recording, on-device AI processing, and wireless communication, reducing centralized computing needs.
• Field tests with Raspberry Pi devices confirmed robust real-time biodiversity monitoring with minimal data processing failures.

acoupi: An Open-Source Python Framework for Edge-Based Bioacoustic AI Deployment

The essence of the paper titled "acoupi: An Open-Source Python Framework for Deploying Bioacoustic AI Models on Edge Devices" concerns the pressing need for scalable and accessible technologies to support biodiversity monitoring, particularly through the use of passive acoustic monitoring (PAM). Technological constraints of traditional systems necessitate frequent manual intervention, large storage capacities, and extensive computational resources. Addressing these limitations, the paper presents acoupi, a framework designed to facilitate the deployment of bioacoustic AI models on edge computing devices, streamlining local data processing and reducing the need for centralized, resource-intensive operations.

Overview and Technical Contributions

The paper offers a comprehensive solution to the complexities involved in deploying AI-based bioacoustic monitoring systems on low-cost hardware such as Raspberry Pi. Central to acoupi is its modular design, which integrates audio recording, on-device AI data processing, efficient data management, and wireless communication within a single, easily customizable platform. This modularity allows researchers to tailor device operations to specific monitoring goals by selecting and configuring suitable components of the workflow.

Key technical innovations include:

• On-Device Processing: By allowing real-time AI-driven analysis directly on the device, acoupi reduces the burden on network and storage resources, transmitting only pertinent data.
• Software Architecture: Utilization of a Python-based framework ensures ease of use and adaptability, facilitating the integration of diverse AI models such as BirdNET and BatDetect2 for avian and bat species classification respectively.
• Command-Line Interface (CLI): An intuitive CLI enhances user experience, obviating the need for in-depth coding knowledge when adjusting configurations.

Practical Deployment and Evaluation

The practical deployment of acoupi involved using Raspberry Pi devices in an urban park environment in the UK. Equipped with the pre-built AI classifiers, BirdNET and BatDetect2, the devices were able to perform real-time monitoring effectively. While minor operational issues were noted—such as minor imprecisions in scheduling—the deployment largely succeeded in its objectives, achieving reliable, consistent data transmission and storage minimization.

The empirical results underscored the system's robustness, manifest in high data processing success rates during the month-long field tests, with a negligible processing failure percentage.

Implications and Future Directions

The adoption of acoupi has significant implications for biodiversity monitoring and conservation efforts. By democratizing access to high-quality sensing capabilities at lower costs, acoupi can enhance ecological research, offering a scalable, adaptable, and efficient tool for monitoring species presence in various habitats. Furthermore, the framework facilitates the integration of real-time ecological insights into conservation strategies, advancing both immediate and long-term ecological research goals.

Looking ahead, acoupi's development trajectory may include extending the hardware compatibility, integrating additional AI models for broader ecological applications, and refining power management capabilities to support deployments in energy-constrained environments. Another promising avenue is the exploration of environmental sensors to provide rich contextual data, enabling synergistic analyses that merge acoustic monitoring with environmental parameters.

In conclusion, "acoupi" represents a significant advancement in bioacoustic monitoring, coupling cutting-edge AI with edge computing to empower global biodiversity efforts. Through its user-centric design and open-source model, it holds the potential to catalyze innovations across the spectrum of ecological inquiry and application.