SM4在被动声学监测中新热带无尾目鸟类识别基准数据集中的应用
Abstract
Global change is predicted to induce shifts in anuran acoustic behavior, which can be studied through passive acoustic monitoring (PAM). Understanding changes in calling behavior requires automatic identification of anuran species, which is challenging due to the particular characteristics of neotropical soundscapes. In this paper, we introduce a large-scale multi-species dataset of anuran amphibians calls recorded by PAM, that comprises 27hours of expert annotations for 42 different species from two Brazilian biomes. We provide open access to the dataset, including the raw recordings, experimental setup code, and a benchmark with a baseline model of the fine-grained categorization problem. Additionally, we highlight the challenges of the dataset to encourage machine learning researchers to solve the problem of anuran call identification towards conservation policy. All our experiments and resources have been made available at https://soundclim.github.io/anuraweb/.
摘要：
全球变化预计会引起无尾声波行为的变化，这可以通过被动声学监测（PAM）来研究。了解呼唤行为的变化需要自动识别无尾目动物物种，由于新热带音景的特殊特征，这是一个挑战。本文介绍了PAM记录的无尾两栖动物叫声的大规模多物种数据集，其中包括来自巴西两个生物群落的42个不同物种的27小时专家注释。我们提供对数据集的开放访问，包括原始记录、实验设置代码和具有细粒度分类问题基线模型的基准。此外，我们强调了数据集的挑战，以鼓励机器学习研究人员解决针对保护政策的anuran呼叫识别问题。我们所有的实验和资源都在https://soundclim.github.io/anuraweb/上面.
关键词：SM4声音记录器，鸟鸣叫监测，Wildlife Acoustics，野外动物声音监测，鸟类监测
