Using a Novel Visualization Tool for Rapid Survey of Long-Duration Acoustic Recordings for Ecological Studies of Frog Chorusing
Using a Novel Visualization Tool for Rapid Survey of Long-Duration Acoustic Recordings for Ecological Studies of Frog Chorusing
复制标题
使用新型可视化工具快速调查长期声学记录以进行青蛙合唱的生态研究
DOI：
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发表时间：
2022
影响因子：
3
通讯作者：
L. Schwarzkopf
中科院分区：
文献类型：
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作者：
Sheryn Brodie;M. Towsey;S. Allen‐Ankins;P. Roe;L. Schwarzkopf
连续记录环境声音可以长期监测有声音的野生动物，并将生态研究扩展到大的时间和空间尺度。然而，这种机会目前受到大型声学数据集分析的限制。呼叫检测的计算方法和自动化需要专家的专业知识，而且开发起来很耗时，因此大多数生物学研究人员继续使用人工聆听和检查频谱图来分析他们的录音。伪彩色光谱图是最近发展起来的一种工具，它允许长时间录音的可视化，旨在帮助生态学家导航他们的音频数据和检测感兴趣的物种。本文探讨了利用这种可视化方法在大量连续录音中识别多种蛙类，并收集蛙类群落合唱活动数据的有效性。我们发现，经过一段时间的观察者训练，青蛙的合唱可以以很高的准确率视觉识别物种。我们提出了一种分析这些数据的方法，包括一个简单的R例程，用于交互式地选择假彩色谱图上的短段，以便快速手动检查视觉识别的声音。我们建议这些方法可以有效地应用于大型声学数据集，以分析其他合唱物种的呼叫模式。
Continuous recording of environmental sounds could allow long-term monitoring of vocal wildlife, and scaling of ecological studies to large temporal and spatial scales. However, such opportunities are currently limited by constraints in the analysis of large acoustic data sets. Computational methods and automation of call detection require specialist expertise and are time consuming to develop, therefore most biological researchers continue to use manual listening and inspection of spectrograms to analyze their sound recordings. False-color spectrograms were recently developed as a tool to allow visualization of long-duration sound recordings, intending to aid ecologists in navigating their audio data and detecting species of interest. This paper explores the efficacy of using this visualization method to identify multiple frog species in a large set of continuous sound recordings and gather data on the chorusing activity of the frog community. We found that, after a phase of training of the observer, frog choruses could be visually identified to species with high accuracy. We present a method to analyze such data, including a simple R routine to interactively select short segments on the false-color spectrogram for rapid manual checking of visually identified sounds. We propose these methods could fruitfully be applied to large acoustic data sets to analyze calling patterns in other chorusing species.