Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge
This addresses the need for automated, robust bird monitoring for ecological assessment, representing a strong specific gain rather than a foundational breakthrough.
The paper tackled the problem of general-purpose acoustic bird detection in remote monitoring by organizing a data challenge, achieving around 88% AUC, which is much higher than previous methods, without manual recalibration or pre-training for specific species or conditions.
Assessing the presence and abundance of birds is important for monitoring specific species as well as overall ecosystem health. Many birds are most readily detected by their sounds, and thus passive acoustic monitoring is highly appropriate. Yet acoustic monitoring is often held back by practical limitations such as the need for manual configuration, reliance on example sound libraries, low accuracy, low robustness, and limited ability to generalise to novel acoustic conditions. Here we report outcomes from a collaborative data challenge showing that with modern machine learning including deep learning, general-purpose acoustic bird detection can achieve very high retrieval rates in remote monitoring data --- with no manual recalibration, and no pre-training of the detector for the target species or the acoustic conditions in the target environment. Multiple methods were able to attain performance of around 88% AUC (area under the ROC curve), much higher performance than previous general-purpose methods. We present new acoustic monitoring datasets, summarise the machine learning techniques proposed by challenge teams, conduct detailed performance evaluation, and discuss how such approaches to detection can be integrated into remote monitoring projects.