1. Assessing diversity of discretely varying behaviour is a classical ethological problem. In particular, the challenge of calculating an individuals’ or species’ vocal repertoire size is often an important step in ecological and behavioural studies, but a reproducible and broadly applicable method for accomplishing this task is not currently available.
2. We offer a generalizable method to automate the calculation and quantification of acoustic diversity using an unsupervised random forest framework. We tested our method using natural and synthetic datasets of known repertoire sizes that exhibit standardized variation in common acoustic features as well as in recording quality. We tested two approaches to estimate acoustic diversity using the output from unsupervised random forest analyses: (a) cluster analysis to estimate the number of discrete acoustic signals (e.g. repertoire size) and (b) an estimation of acoustic area in acoustic feature space, as a proxy for repertoire size.
3. We find that our unsupervised analyses classify acoustic structure with high accuracy. Specifically, both approaches accurately estimate element diversity when repertoire size is small to intermediate (5–20 unique elements). However, for larger datasets (20–100 unique elements), we find that calculating the size of the area occupied in acoustic space is a more reliable proxy for estimating repertoire size.
4. We conclude that our implementation of unsupervised random forest analysis offers a generalizable tool that researchers can apply to classify acoustic structure of diverse datasets. Additionally, output from these analyses can be used to compare the distribution and diversity of signals in acoustic space, creating opportunities to quantify and compare the amount of acoustic variation among individuals, populations or species in a standardized way. We provide R code and examples to aid researchers interested in using these techniques.

中文翻译：

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一种用于分类和量化声学多样性的机器学习方法

1. 评估离散变化行为的多样性是一个经典的行为学问题。特别是，计算个人或物种的声音曲目大小的挑战通常是生态和行为研究中的一个重要步骤，但目前还没有一种可重复且广泛适用的方法来完成这项任务。
2. 我们提供了一种通用的方法来使用无监督的随机森林框架自动计算和量化声学多样性。我们使用已知曲目大小的自然和合成数据集测试了我们的方法，这些数据集在常见的声学特征和录音质量方面表现出标准化的变化。我们使用无监督随机森林分析的输出测试了两种估计声学多样性的方法：（a）聚类分析来估计离散声学信号的数量（例如曲目大小）和（b）声学特征空间中声学区域的估计，如曲目大小的代理。
3. 我们发现我们的无监督分析对声学结构进行了高精度分类。具体而言，当曲目大小为中小（5-20​​ 个独特元素）时，这两种方法都可以准确估计元素多样性。然而，对于更大的数据集（20-100 个唯一元素），我们发现计算声学空间中占据的区域的大小是估计曲目大小的更可靠的代理。
4. 我们得出的结论是，我们对无监督随机森林分析的实施提供了一种可推广的工具，研究人员可以将其应用于对不同数据集的声学结构进行分类。此外，这些分析的输出可用于比较声学空间中信号的分布和多样性，创造机会以标准化的方式量化和比较个体、种群或物种之间的声学​​变化量。我们提供 R 代码和示例来帮助对使用这些技术感兴趣的研究人员。