RT Journal Article
SR Electronic
T1 BirdVox: Machine listening for bird migration monitoring
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.05.31.494155
DO 10.1101/2022.05.31.494155
A1 Lostanlen, Vincent
A1 Cramer, Aurora
A1 Salamon, Justin
A1 Farnsworth, Andrew
A1 Van Doren, Benjamin M.
A1 Kelling, Steve
A1 Bello, Juan Pablo
YR 2022
UL http://biorxiv.org/content/early/2022/05/31/2022.05.31.494155.abstract
AB The steady decline of avian populations worldwide urgently calls for a cyber-physical system to monitor bird migration at the continental scale. Compared to other sources of information (radar and crowdsourced observations), bioacoustic sensor networks combine low latency with a high taxonomic specificity. However, the scarcity of flight calls in bioacoustic monitoring scenes (below 0.1% of total recording time) requires the automation of audio content analysis. In this article, we address the problem of scaling up the detection and classification of flight calls to a full-season dataset: 6672 hours across nine sensors, yielding around 480 million neural network predictions. Our proposed pipeline, BirdVox, combines multiple machine learning modules to produce per-species flight call counts. We evaluate BirdVox on an annotated subset of the full season (296 hours) and discuss the main sources of estimation error which are inherent to a real-world deployment: mechanical sensor failures, sensitivity to background noise, misdetection, and taxonomic confusion. After developing dedicated solutions to mitigate these sources of error, we demonstrate the usability of BirdVox by reporting a species-specific temporal estimate of flight call activity for the Swainson’s Thrush (Catharus ustulatus).Competing Interest StatementThe authors have declared no competing interest.