Chronister Lauren M, Rhinehart Tessa A, Place Aidan, Kitzes Justin
Department of Biological Sciences, University of Pittsburgh, Pittsburgh, 15260, Pennsylvania, USA.
Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, 15213, Pennsylvania, USA.
Ecology. 2021 Jun;102(6):e03329. doi: 10.1002/ecy.3329. Epub 2021 May 11.
Acoustic recordings of soundscapes are an important category of audio data that 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 data sets of soundscape recordings are publicly available. Further still, there are no publicly available strongly labeled soundscape recordings of bird sounds that contain information on timing, frequency, and species. Therefore, we present the first data set 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, Pennsylvania, USA. 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 data set may be numerous and include the training, validation, and testing of certain advanced machine-learning models that detect or classify bird sounds. There are no copyright or propriety restrictions; please cite this paper when using materials within.
声景的声学录音是一类重要的音频数据，可用于回答各种问题，并且生态学中一个被称为“声景生态学”的完整学科已经兴起以对其进行研究。由于鸟类在大多数陆地环境中无处不在且其发声活动频繁，鸟鸣声常常是声景研究的重点。自主声学记录仪增加了自然声景录音的数量和可获取性，同时减轻了人类观察者对群落行为的影响。然而，如果不对其中包含的声音进行分析，此类录音就没什么用处。目前，人工分析是处理这种数据形式以用于声景生态学中某些应用的最佳方法，但这是一项艰巨的任务，有时需要数小时的人工审查才能处理相对较少时长的录音。因此，很少有带注释的声景录音数据集可供公开获取。更进一步说，目前还没有公开可用的、带有鸟鸣声的强标记声景录音，这些录音包含有关时间、频率和物种的信息。因此，我们提供了第一个在免费使用许可下的强标记鸟鸣声声景录音数据集。这些数据是在美国宾夕法尼亚州雷克托的粉末磨坊自然保护区收集的。录音涵盖了2018年4月至7月期间由自主声学记录仪收集的385分钟黎明合唱录音。在研究期间的四天里连续进行了录音，包含48个物种和16052条注释。该数据集的应用可能有很多，包括训练、验证和测试某些检测或分类鸟鸣声的先进机器学习模型。不存在版权或所有权限制；使用其中的材料时请引用本文。
