NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe

The persisting threats on migratory bird populations highlights the urgent need for effective monitoring techniques that could assist in their conservation. Among these, passive acoustic monitoring is an essential tool, particularly for nocturnal migratory species that are difficult to track otherwise. This work presents the Nocturnal Bird Migration (NBM) dataset, a collection of 13,359 annotated vocalizations from 117 species of the Western Palearctic. The dataset includes precise time and frequency annotations, gathered by dozens of bird enthusiasts across France, enabling novel downstream acoustic analysis. In particular, we demonstrate that a two-stage object detection model, tailored for the processing of audio data, can be trained on our dataset to retrieve localized bounding box coordinates around each signal of interest in a spectrogram. This object detection approach, which is largely overlooked in the bird sound recognition literature, allows important applications by potentially differentiating individual birds within audio windows. Further, we show that the accuracy of our recognition model on the 45 main species of the dataset competes with state-of-the-art systems trained on much larger datasets. This highlights the interest of fostering similar open-science initiatives to acquire costly but valuable fine-grained annotations of audio files. All data and code are made openly available.