Smeele Simeon Q, Tyndel Stephen A, Klump Barbara C, Alarcón-Nieto Gustavo, Aplin Lucy M
Cognitive & Cultural Ecology Research Group Max Planck Institute of Animal Behavior Radolfzell Germany.
Department of Human Behavior, Ecology and Culture Max Planck Institute for Evolutionary Anthropology Leipzig Germany.
Ecol Evol. 2024 May 23;14(5):e11384. doi: 10.1002/ece3.11384. eCollection 2024 May.
To better understand how vocalisations are used during interactions of multiple individuals, studies are increasingly deploying on-board devices with a microphone on each animal. The resulting recordings are extremely challenging to analyse, since microphone clocks drift non-linearly and record the vocalisations of non-focal individuals as well as noise. Here we address this issue with callsync, an R package designed to align recordings, detect and assign vocalisations to the caller, trace the fundamental frequency, filter out noise and perform basic analysis on the resulting clips. We present a case study where the pipeline is used on a dataset of six captive cockatiels () wearing backpack microphones. Recordings initially had a drift of ~2 min, but were aligned to within ~2 s with our package. Using callsync, we detected and assigned 2101 calls across three multi-hour recording sessions. Two had loud beep markers in the background designed to help the manual alignment process. One contained no obvious markers, in order to demonstrate that markers were not necessary to obtain optimal alignment. We then used a function that traces the fundamental frequency and applied spectrographic cross correlation to show a possible analytical pipeline where vocal similarity is visually assessed. The callsync package can be used to go from raw recordings to a clean dataset of features. The package is designed to be modular and allows users to replace functions as they wish. We also discuss the challenges that might be faced in each step and how the available literature can provide alternatives for each step.
为了更好地理解在多个个体互动过程中发声是如何被使用的，越来越多的研究在每只动物身上部署带有麦克风的车载设备。由于麦克风时钟会非线性漂移，并且会记录非焦点个体的发声以及噪音，因此对由此产生的录音进行分析极具挑战性。在这里，我们使用callsync来解决这个问题，它是一个R包，旨在对齐录音、检测发声并将其分配给呼叫者、追踪基频、滤除噪音以及对所得音频片段进行基本分析。我们展示了一个案例研究，其中该流程被应用于一个由六只戴着背包式麦克风的圈养鸡尾鹦鹉组成的数据集。录音最初有大约2分钟的漂移，但使用我们的包后被对齐到了大约2秒以内。通过使用callsync，我们在三个长达数小时的录音时段中检测并分配了2101次叫声。其中两个在背景中有响亮的哔哔标记，旨在帮助人工对齐过程。另一个没有明显的标记，以证明标记对于获得最佳对齐并非必要。然后，我们使用了一个追踪基频的函数，并应用频谱互相关来展示一个可能的分析流程，在这个流程中可以直观地评估发声的相似性。callsync包可用于从原始录音生成一个干净的特征数据集。该包设计为模块化的，允许用户根据自己的意愿替换函数。我们还讨论了每个步骤可能面临的挑战以及现有文献如何为每个步骤提供替代方法。
