Burchardt Lara S, Briefer Elodie F, Knörnschild Mirjam
Museum für Naturkunde - Leibniz Institute for Evolution and Biodiversity Science Berlin Germany.
Institute of Animal Behavior Freie Universität Berlin Berlin Germany.
Ecol Evol. 2021 Dec 6;11(24):18229-18237. doi: 10.1002/ece3.8417. eCollection 2021 Dec.
The temporal structure of animals' acoustic signals can inform about context, urgency, species, individual identity, or geographical origin. We present three independent ideas to further expand the applicability of rhythm analysis for isochronous, that is, metronome-like, rhythms. A description of a rhythm or beat needs to include a description of its goodness of fit, meaning how well the rhythm describes a sequence. Existing goodness-of-fit values are not comparable between methods and datasets. Furthermore, they are strongly correlated with certain parameters of the described sequence, for example, the number of elements in the sequence. We introduce a new universal goodness-of-fit value, , comparable across methods and datasets, which illustrates how well a certain beat frequency in Hz describes the temporal structure of a sequence of elements. We then describe two additional approaches to adapt already existing methods to analyze the rhythm of acoustic sequences of animals. The new additions, a slightly modified way to use the already established Fourier analysis and concrete examples on how to use the visualization with recurrence plots, enable the analysis of more variable data, while giving more details than previously proposed measures. New methods are tested on 6 datasets including the very complex flight songs of male skylarks. The is the first goodness-of-fit value capable of giving the information per element, instead of only per sequence. Advantages and possible interpretations of the new approaches are discussed. The new methods enable the analysis of more variable and complex communication signals. They give indications on which levels and structures to analyze and enable to track changes and differences in individuals or populations, for instance, during ontogeny or across regions. Especially, the is not restricted to the analysis of acoustic signals but could for example also be applied on heartbeat measurements. Taken together, the and proposed method additions greatly broaden the scope of rhythm analysis methods.
动物声学信号的时间结构能够传达有关环境、紧迫性、物种、个体身份或地理起源的信息。我们提出了三个独立的想法,以进一步扩大节奏分析在等时节奏(即类似节拍器的节奏)方面的适用性。对节奏或节拍的描述需要包括对其拟合优度的描述,即节奏对序列的描述程度。现有方法和数据集之间的拟合优度值不可比。此外,它们与所描述序列的某些参数密切相关,例如序列中的元素数量。我们引入了一个新的通用拟合优度值,该值在不同方法和数据集之间具有可比性,它说明了以赫兹为单位的特定节拍频率对元素序列时间结构的描述程度。然后,我们描述了另外两种方法,以调整现有的方法来分析动物声学序列的节奏。新增的内容包括一种对已建立的傅里叶分析稍加修改的使用方式,以及关于如何使用递归图进行可视化的具体示例,这些能够分析更多可变数据,同时比之前提出的方法提供更多细节。新方法在6个数据集上进行了测试,包括雄性云雀非常复杂的飞行歌声。这个新的拟合优度值是第一个能够给出每个元素信息的,而不仅仅是每个序列的信息。我们讨论了新方法的优点和可能的解释。新方法能够分析更多可变和复杂的通信信号。它们指出了应该在哪些层次和结构上进行分析,并能够跟踪个体或种群在发育过程中或跨区域的变化和差异。特别是,这个新的拟合优度值不仅限于声学信号的分析,例如还可以应用于心跳测量。总之,这个新的拟合优度值和提出的方法补充极大地拓宽了节奏分析方法的范围。