DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals.
DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals.
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DISCO：一种深度学习集成，用于对声学信号进行不确定性感知分割。
DOI：
10.1101/2023.01.24.525459
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Wheeler,TravisJ
中科院分区：
文献类型：
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作者：
Colligan,Thomas;Irish,Kayla;Emlen,DouglasJ;Wheeler,TravisJ
动物声音的记录使我们能够对动物的交流、行为和多样性进行广泛的观察调查。在这些录音中自动标记声音事件可以提高分析的吞吐量和再现性。在这里，我们描述了我们的软件包，用于标记动物声音录音中的元素，并演示了它在甲虫求爱和鲸鱼歌曲录音中的实用性。DISCO软件计算合理的置信度估计，并产生高精度和准确性的标签。除了核心标注软件之外，它还提供了一个简单的工具来标注训练数据，以及一个可视化系统来分析生成的标签。DISCO是开源的，易于安装，它可以与标准文件格式一起工作，并且它的使用门槛很低。
Recordings of animal sounds enable a wide range of observational inquiries into animal communication, behavior, and diversity. Automated labeling of sound events in such recordings can improve both throughput and reproducibility of analysis. Here, we describe our software package for labeling elements in recordings of animal sounds, and demonstrate its utility on recordings of beetle courtships and whale songs. The software, DISCO, computes sensible confidence estimates and produces labels with high precision and accuracy. In addition to the core labeling software, it provides a simple tool for labeling training data, and a visual system for analysis of resulting labels. DISCO is open-source and easy to install, it works with standard file formats, and it presents a low barrier of entry to use.