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An annotated set of audio recordings of Eastern North American birds containing frequency, time, and species information

Chronister, Lauren M.1; Rhinehart, Tessa A.1; Place, Aidan2; Kitzes, Justin1

Published Apr 06, 2021 on Dryad. https://doi.org/10.5061/dryad.d2547d81z

Data files

Apr 01, 2021 version files 1.61 GB
Apr 06, 2021 version files 1.18 GB

Abstract

Acoustic recordings of soundscapes are an important category of audio data which can be useful for answering a variety of questions, and an entire discipline within ecology, dubbed “soundscape ecology,” has risen to study them. Bird sound is often the focus of studies of soundscapes due to the ubiquitousness of birds in most terrestrial environments and their high vocal activity. Autonomous acoustic recorders have increased the quantity and availability of recordings of natural soundscapes while mitigating the impact of human observers on community behavior. However, such recordings are of little use without analysis of the sounds they contain. Manual analysis currently stands as the best means of processing this form of data for use in certain applications within soundscape ecology, but it is a laborious task, sometimes requiring many hours of human review to process comparatively few hours of recording. For this reason, few annotated datasets of soundscape recordings are publicly available. Further still, there are no publicly available strongly-labeled soundscape recordings of bird sounds which contain information on timing, frequency, and species. Therefore, we present the first dataset of strongly-labeled bird sound soundscape recordings under free use license. These data were collected in the Northeastern United States at Powdermill Nature Reserve, Rector, PA. Recordings encompass 385 minutes of dawn chorus recordings collected by autonomous acoustic recorders between the months of April through July 2018. Recordings were collected in continuous bouts on four days during the study period, and contain 48 species and 16,052 annotations. Applications of this dataset may be numerous, and include the training, validation, and testing of certain advanced machine learning models which detect or classify bird sounds.