Comparison of methods for rhythm analysis of complex animals’ acoustic signals

Analyzing the rhythm of animals’ acoustic signals is of interest to a growing number of researchers: evolutionary biologists want to disentangle how these structures evolved and what patterns can be found, and ecologists and conservation biologists aim to discriminate cryptic species on the basis of parameters of acoustic signals such as temporal structures. Temporal structures are also relevant for research on vocal production learning, a part of which is for the animal to learn a temporal structure. These structures, in other words, these rhythms, are the topic of this paper. How can they be investigated in a meaningful, comparable and universal way? Several approaches exist. Here we used five methods to compare their suitability and interpretability for different questions and datasets and test how they support the reproducibility of results and bypass biases. Three very different datasets with regards to recording situation, length and context were analyzed: two social vocalizations of Neotropical bats (multisyllabic, medium long isolation calls of Saccopteryx bilineata, and monosyllabic, very short isolation calls of Carollia perspicillata) and click trains of sperm whales, Physeter macrocephalus. Techniques to be compared included Fourier analysis with a newly developed goodness-of-fit value, a generate-and-test approach where data was overlaid with varying artificial beats, and the analysis of inter-onset-intervals and calculations of a normalized Pairwise Variability Index (nPVI). We discuss the advantages and disadvantages of the methods and we also show suggestions on how to best visualize rhythm analysis results. Furthermore, we developed a decision tree that will enable researchers to select a suitable and comparable method on the basis of their data.越来越多的研究者关注动物声学信号的节律分析：进化生物学家旨在厘清这类声学结构的演化路径与可观测模式，生态学家与保护生物学家则希望借助声学信号的时域结构（temporal structures）等参数，甄别隐存物种。时域结构同样与发声学习研究相关，其中一类研究聚焦于动物对时域结构的学习行为。换言之，这类结构即节律，正是本文的研究主题。那么，如何以兼具意义性、可比性与普适性的方式开展此类研究？ 目前已有多种研究路径可供选择。本文采用五种方法，针对不同研究问题与数据集对比其适用性与可解释性，并检验这些方法如何保障研究结果的可重复性并规避研究偏倚。 本研究分析了三类在录制场景、时长与语境上差异显著的数据集：新热带蝙蝠的两类社交发声（白线蝠Saccopteryx bilineata的多音节中等时长分离鸣叫，以及短尾叶鼻蝠Carollia perspicillata的单音节极短分离鸣叫），以及抹香鲸Physeter macrocephalus的咔哒声序列。 本次对比的分析技术包括：带有新开发拟合优度值（goodness-of-fit value）的傅里叶分析（Fourier analysis）、将数据集与不同人工节拍叠加的生成-测试法（generate-and-test approach）、发声间隔（inter-onset-intervals）分析，以及归一化成对变异指数（normalized Pairwise Variability Index，nPVI）的计算。 本文讨论了各方法的优劣，并针对节律分析结果的最优可视化方案提出建议。此外，本文还构建了一套决策树，可帮助研究者基于自身数据集选择适配且具备可比性的分析方法。