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We are sharing the Ecoacoustic Dataset from Arctic North Slope Alaska (EDANSA-2019), a dataset with audio samples collected from the area of 9000 square miles throughout the 2019 summer season on the North Slope of Alaska and neighboring regions.</p> There are over 27 hours of labeled data according to 28 tags with enough instances of 9 important environmental classes to train baseline convolutional recognizers.</p> Please see the following GitHub page for the accompanying publication, updates about the dataset, and baseline code: https://github.com/speechLabBcCuny/EDANSA-2019 </p> 

The arctic is warming at three times the rate of the global average, affecting the habitat and lifecycles of migratory species that reproduce there, like birds and caribou. Ecoacoustic monitoring can help efficiently track changes in animal phenology and behavior over large areas so that the impacts of climate change on these species can be better understood and potentially mitigated. We introduce here the Ecoacoustic Dataset from Arctic North Slope Alaska (EDANSA-2019), a dataset collected by a network of 100 autonomous recording units covering an area of 9000 square miles over the course of the 2019 summer season on the North Slope of Alaska and neighboring regions. We labeled over 27 hours of this dataset according to 28 tags with enough instances of 9 important environmental classes to train baseline convolutional recognizers. We are releasing this dataset and the corresponding baseline to the community to accelerate the recognition of these sounds and facilitate automated analyses of large-scale ecoacoustic databases. 

Arctic boreal forests are warming at a rate 2–3 times faster than the global average. It is important to understand the effects of this warming on the activities of animals that migrate to these environments annually to reproduce. Acoustic sensors can monitor a wide area relatively cheaply, producing large amounts of data that need to be automatically analyzed. In such scenarios, only a small proportion of the recorded data can be labeled by hand, thus we explore two methods for utilizing labels more efficiently: self-supervised learning using wav2vec 2.0 and data valuation using k-nearest neighbors approximations to compute Shapley values. We confirm that data augmentation and global temporal pooling improve performance by more than 30%, demonstrate for the first time the utility of Shapley data valuation for audio classification, and find that our wav2vec 2.0 model trained from scratch does not improve performance. 

Sound provides a valuable tool for long-term monitoring of sensitive animal habitats at a spatial scale larger than camera traps or field observations, while also providing more details than satellite imagery. Currently, the ability to collect such recordings outstrips the ability to analyze them manually, necessitating the development of automatic analysis methods. While several datasets and models of large corpora of video soundtracks have recently been released, it is not clear to what extent these models will generalize to environmental recordings and the scientific questions of interest in analyzing them. This paper investigates this generalization in several ways and finds that models themselves display limited performance, however, their intermediate representations can be used to train successful models on small sets of labeled data.