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Assessing the presence and abundance of birds is important for monitoringspecific species as well as overall ecosystem health. Many birds are mostreadily detected by their sounds, and thus passive acoustic monitoring ishighly appropriate. Yet acoustic monitoring is often held back by practicallimitations such as the need for manual configuration, reliance on examplesound libraries, low accuracy, low robustness, and limited ability togeneralise to novel acoustic conditions. Here we report outcomes from acollaborative data challenge showing that with modern machine learningincluding deep learning, general-purpose acoustic bird detection can achievevery high retrieval rates in remote monitoring data --- with no manualrecalibration, and no pre-training of the detector for the target species orthe acoustic conditions in the target environment. Multiple methods were ableto attain performance of around 88% AUC (area under the ROC curve), much higherperformance than previous general-purpose methods. We present new acousticmonitoring datasets, summarise the machine learning techniques proposed bychallenge teams, conduct detailed performance evaluation, and discuss how suchapproaches to detection can be integrated into remote monitoring projects.

Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge