Data and code to run birdnet-discovery, a pipeline for signal discovery and training dataset creation using BirdNET embeddings, including example data from acoustic ARUs in Northern Alaska

In recent years, deep learning has become a popular solution for
processing large ecological monitoring datasets. This rise in use has
resulted in global classification models for a variety of data types and
taxa, such as BirdNET, which classifies vocalizations of more than 6,000
avian species from acoustic data. These global models can be useful
pre-trained models for transfer learning, allowing researchers to more
easily develop classifiers specialized to their datasets. However, the
development of such models hinges on the availability of comprehensive,
high-quality training data, which can be difficult to acquire, produce,
and use. We present a novel pipeline for creating training data from a
large and unlabeled dataset with minimal human oversight. We used this
pipeline and BirdNET as our base model to develop a
transfer-learning-based model, ArcticSoundsNET, using acoustic monitoring
data from 205 sites across Alaska’s Arctic Coastal Plain. We compared
performance of ArcticSoundsNET with that of BirdNET to evaluate the
effectiveness of our pipeline and success of the new model. We found that
the ability of ArcticSoundsNET to detect and classify avian vocalizations
in our data exceeded that of BirdNET by several orders of magnitude (AUC =
0.299 for ArcticSoundsNET, AUC = <0.001 for BirdNET). Importantly,
our method for developing a training dataset is widely applicable for
ecologists who do not have large amounts of labeled data, facilitating the
creation of task-specific classification models. Developing such models is
an essential step in using large acoustic datasets to support ecological
conservation of critical species and habitats.