TUHH Open Research
 

(DEMO) Real-time bird detection with a modular acoustic sensing platform

Start Page
361
End Page
363
Citation
22nd Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2026
Contribution to Conference
22nd Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2026  
Publisher DOI
10.1109/DCOSS-IoT69657.2026.00065
Scopus ID
2-s2.0-105049693516
Publisher
IEEE
ISBN of container
979-833154670-0
Passive Acoustic Monitoring (PAM) enables continuous, non-invasive observation of wildlife and is widely used for biodiversity assessment. Long-term deployments require sensing platforms that are reliable, energy-efficient, affordable, and flexible for future extensions such as embedded inference.This demo paper presents a real-time bird detection system built around a modular embedded acoustic sensing platform for low-power environmental audio recording. The live demonstration shows the workflow from acoustic acquisition to automated BirdNET-based species inference. Bird vocalizations are recorded by the sensing platform, transferred to a connected host system, and analyzed using BirdNET to visualize detected species, confidence values, and temporal activity patterns in real time.The demonstration showcases an end-to-end low-power bioacoustic monitoring workflow, combining autonomous environmental audio acquisition, embedded sensing hardware, and real-time BirdNET-based species inference within a compact modular platform.
Subjects
audio systems
Biodiversity
data acquisition
edge AI
low power electronics
microcontrollers
wireless sensor networks
DDC Class
621.3: Electrical Engineering, Electronic Engineering