Drone audition for bioacoustic monitoring

• Published in: Methods in ecology and evolution Vol. 14; no. 12; pp. 3068 - 3082
• Main Authors: Wang, Lin, Clayton, Michael, Rossberg, Axel G.
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01-12-2023; Wiley
• Subjects: Acoustic tracking; Acoustics; aerial survey; Algorithms; Altitude; bioacoustic monitoring; Biodiversity; bird recognition; Birds; Cameras; drone audition; ego‐noise reduction; Environmental monitoring; Flight training; Hardware; Hovering; Hovering flight; Listening; Machine learning; Microphones; Monitoring; New technology; Noise levels; Noise monitoring; Performance degradation; Performance evaluation; Propellers; Recording; Rotating bodies; Sensors; Shotguns; Software; Sound; Sound recordings; Target masking; Wildlife conservation; Wire rope
• ISSN: 2041-210X; 2041-210X
• DOI: 10.1111/2041-210X.14234

Multi‐rotor drones equipped with acoustic sensors have great potential for bioacoustically monitoring vocal species in the environment for biodiversity conservation. The bottleneck of this emerging technology is the ego‐noise from the rotating motors and propellers, which can completely mask the target sound and make sound recordings unusable for further analysis. The ego‐noise not only degrades the performance of bioacoustic monitoring but also impacts the behaviour of target species if the drone is too close to the target area. In this paper, we address this challenging problem by combining hardware and software solutions that minimize the impact of drone ego‐noise on bioacoustic monitoring.
To collect the target sound from the ground, we used a shotgun microphone recording system suspended underneath the drone body with a wire rope (steel fishing line) of length 2 m. The suspended rope puts a large distance between the drone and the recorder, reducing the propeller sound perceived by the microphone. The shotgun microphone enables the sound to be picked up from the ground effectively while rejecting the drone sound from above. We further developed a software solution that aims to automatically recognize the bird species from the bird call recording and we proposed a noise‐augmented training scheme to improve the robustness of bird recognition in the presence of strong drone noise.
We evaluated the performance of the system in a test problem of recognizing 20 bird species with in‐flight recordings, where a loudspeaker on the ground simulates bird calls. The recordings were obtained using a drone hovering at various altitudes ranging from 5 to 30 m. By combining the hardware and software solutions, the system recognizes birds robustly at an altitude of 30 m and signal‐to‐noise ratio −25 dB.
This demonstrates the feasibility of our drone audition system for bioacoustic monitoring. The proposed method overcomes a long‐standing bottleneck problem in drone audition and promises new applications of bioacoustic monitoring in research and management.