PT - JOURNAL ARTICLE AU - Vincent Lostanlen AU - Aurora Cramer AU - Justin Salamon AU - Andrew Farnsworth AU - Benjamin M. Van Doren AU - Steve Kelling AU - Juan Pablo Bello TI - BirdVox: Machine listening for bird migration monitoring AID - 10.1101/2022.05.31.494155 DP - 2022 Jan 01 TA - bioRxiv PG - 2022.05.31.494155 4099 - http://biorxiv.org/content/early/2022/05/31/2022.05.31.494155.short 4100 - http://biorxiv.org/content/early/2022/05/31/2022.05.31.494155.full 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.