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Bird@Edge: Bird Species Recognition at the Edge

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Networked Systems (NETYS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13464))

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Abstract

We present Bird@Edge, an Edge AI system for recognizing bird species in audio recordings to support real-time biodiversity monitoring. Bird@Edge is based on embedded edge devices operating in a distributed system to enable efficient, continuous evaluation of soundscapes recorded in forests. Multiple ESP32-based microphones (called Bird@Edge Mics) stream audio to a local Bird@Edge Station, on which bird species recognition is performed. The results of several Bird@Edge Stations are transmitted to a backend cloud for further analysis, e.g., by biodiversity researchers. To recognize bird species in soundscapes, a deep neural network based on the EfficientNet-B3 architecture is trained and optimized for execution on embedded edge devices and deployed on a NVIDIA Jetson Nano board using the DeepStream SDK. Our experiments show that our deep neural network outperforms the state-of-the-art BirdNET neural network on several data sets and achieves a recognition quality of up to 95.2% mean average precision on soundscape recordings in the Marburg Open Forest, a research and teaching forest of the University of Marburg, Germany. Measurements of the power consumption of the Bird@Edge components highlight the real-world applicability of the approach. All software and firmware components of Bird@Edge are available under open source licenses.

J. Höchst and H. Bellafkir—These authors contributed equally.

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Notes

  1. 1.

    https://github.com/umr-ds/BirdEdge.

  2. 2.

    https://github.com/kahst/BirdNET-Lite.

  3. 3.

    https://www.knowles.com/docs/default-source/default-document-library/sph0645lm4h-1-datasheet.pdf.

  4. 4.

    https://github.com/umr-ds/BirdEdge/tree/main/BirdEdge-Client.

  5. 5.

    https://grafana.com.

  6. 6.

    https://github.com/umr-ds/BirdAtEdge-OS.

  7. 7.

    https://developer.nvidia.com/tensorrt.

  8. 8.

    https://developer.nvidia.com/deepstream-sdk.

  9. 9.

    https://github.com/mcguirepr89/BirdNET-Pi.

  10. 10.

    https://xeno-canto.org/706150.

  11. 11.

    https://www.msoon.com/high-voltage-power-monitor.

  12. 12.

    https://www.sparkfun.com/products/15663.

  13. 13.

    https://github.com/umr-ds/BirdEdge.

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Acknowledgments

This work is funded by the Hessian State Ministry for Higher Education, Research and the Arts (HMWK) (LOEWE Natur 4.0, LOEWE emergenCITY, and hessian.AI Connectom AI4Birds), the German Academic Exchange Service (DAAD) (Transformation Partnership Program; Project OLIVIA), and the German Research Foundation (DFG, Project 210487104 - Collaborative Research Center SFB 1053 MAKI).

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany

    Jonas Höchst, Hicham Bellafkir, Patrick Lampe, Markus Vogelbacher, Markus Mühling, Daniel Schneider & Bernd Freisleben

  2. Department of Biology, University of Marburg, Marburg, Germany

    Kim Lindner, Sascha Rösner, Dana G. Schabo & Nina Farwig

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  1. Jonas Höchst
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  2. Hicham Bellafkir
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Correspondence to Jonas Höchst .

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  1. Mohammed First University, Oujda, Morocco

    Mohammed-Amine Koulali

  2. Technical University of Darmstadt, Darmstadt, Germany

    Mira Mezini

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Höchst, J. et al. (2022). Bird@Edge: Bird Species Recognition at the Edge. In: Koulali, MA., Mezini, M. (eds) Networked Systems. NETYS 2022. Lecture Notes in Computer Science, vol 13464. Springer, Cham. https://doi.org/10.1007/978-3-031-17436-0_6

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  • DOI: https://doi.org/10.1007/978-3-031-17436-0_6

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