Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California, United States of America.
PLoS Comput Biol. 2020 Jan 13;16(1):e1007598. doi: 10.1371/journal.pcbi.1007598. eCollection 2020 Jan.
Passive acoustic monitoring has become an important data collection method, yielding massive datasets replete with biological, environmental and anthropogenic information. Automated signal detectors and classifiers are needed to identify events within these datasets, such as the presence of species-specific sounds or anthropogenic noise. These automated methods, however, are rarely a complete substitute for expert analyst review. The ability to visualize and annotate acoustic events efficiently can enhance scientific insights from large, previously intractable datasets. A MATLAB-based graphical user interface, called DetEdit, was developed to accelerate the editing and annotating of automated detections from extensive acoustic datasets. This tool is highly-configurable and multipurpose, with uses ranging from annotation and classification of individual signals or signal-clusters and evaluation of signal properties, to identification of false detections and false positive rate estimation. DetEdit allows users to step through acoustic events, displaying a range of signal features, including time series of received levels, long-term spectral averages, time intervals between detections, and scatter plots of peak frequency, RMS, and peak-to-peak received levels. Additionally, it displays either individual, or averaged sound pressure waveforms, and power spectra within each acoustic event. These views simultaneously provide analysts with signal-level detail and encounter-level context. DetEdit creates datasets of signal labels for further analyses, such as training classifiers and quantifying occurrence, abundances, or trends. Although designed for evaluating underwater-recorded odontocete echolocation click detections, DetEdit can be adapted to almost any stereotyped impulsive signal. Our software package complements available tools for the bioacoustic community and is provided open source at https://github.com/MarineBioAcousticsRC/DetEdit.
被动声学监测已成为一种重要的数据收集方法，产生了大量充满生物、环境和人为信息的数据集。需要自动信号探测器和分类器来识别这些数据集中的事件，例如特定物种声音的存在或人为噪声。然而，这些自动化方法很少能完全替代专家分析师的审查。能够有效地可视化和注释声学事件可以增强从以前难以处理的大型数据集获得的科学见解。开发了一个基于 MATLAB 的图形用户界面，称为 DetEdit，用于加速对广泛声学数据集的自动检测进行编辑和注释。该工具高度可配置且用途广泛，可用于注释和分类单个信号或信号簇、评估信号属性，以及识别误报和估计误报率。DetEdit 允许用户逐步浏览声学事件，显示一系列信号特征，包括接收电平的时间序列、长期谱平均值、检测之间的时间间隔以及峰值频率、RMS 和峰峰值接收电平的散点图。此外，它还显示每个声学事件中的单个或平均声压波形和功率谱。这些视图同时为分析师提供了信号级别的详细信息和事件级别的上下文。DetEdit 为进一步分析创建信号标签数据集，例如训练分类器和量化出现、丰度或趋势。尽管专为评估水下记录的齿鲸回声定位点击检测而设计，但 DetEdit 几乎可以适应任何定型脉冲信号。我们的软件包补充了生物声学界现有的工具，并在 https://github.com/MarineBioAcousticsRC/DetEdit 上提供开源。
