Data from: Vocalizations in the plains zebra (Equus quagga)

Acoustic signals are vital in animal communication, and quantifying these signals them is fundamental for understanding animal behaviour and ecology. Vocaliszations can be classified into acoustically and functionally or contextually distinct categories, but establishing these categories can be challenging. Newly developed methods, such as machine learning, can provide solutions for classification tasks. The plains zebra is known for its loud and specific vocaliszations, yet limited knowledge exists on the structure and information content of its vocaliszations. In this study, we employed both feature-based and spectrogram-based algorithms, incorporating supervised and unsupervised machine learning methods to enhance robustness in categoriszing zebra vocaliszation types. Additionally, we implemented a permuted discriminant function analysis (pDFA) to examine the individual identity information contained in the identified vocaliszation types. The findings revealed at least four distinct vocaliszation types he '"snort'," the '"soft snort'," the '"squeal'," and the '"quagga quagga'" with individual differences observed mostly in snorts, and to a lesser extent in squeals. Analyses based on acoustic features outperformed those based on spectrograms, but each excelled in characteriszing different vocaliszation types. We thus recommend the combined use of these two approaches. OuThisr study offers valuable insights into plains zebra vocaliszation, with implications for future comprehensive explorations in animal communication.

声学信号在动物交流中至关重要，对这类信号进行量化是理解动物行为与生态学的基础。发声行为（vocalizations）可按声学属性、功能或背景语境划分为不同类别，但确立这些类别颇具挑战。新兴的机器学习方法可为分类任务提供解决方案。平原斑马（plains zebra）以其洪亮且极具特异性的发声行为著称，但目前学界对其发声的结构与信息内涵的认知仍较为有限。本研究同时采用基于特征与基于声谱图（spectrogram）的算法，融合监督式与非监督式机器学习方法，以提升斑马发声类型分类的鲁棒性。此外，本研究还采用置换判别函数分析（permuted discriminant function analysis, pDFA），以检验已识别的发声类型中所蕴含的个体身份信息。研究结果表明，平原斑马至少存在4种不同的发声类型：即“哼鸣”（snort）、“轻哼”（soft snort）、“尖叫”（squeal）与“夸加鸣”（quagga quagga），其中个体差异在哼鸣中最为显著，在尖叫中次之。基于声学特征的分析效果优于基于声谱图的分析，但二者各自在表征不同发声类型时表现更优。因此我们建议联合使用这两种分析方法。本研究为平原斑马的发声行为研究提供了宝贵见解，可为未来动物交流领域的全面探索提供参考。

搜集汇总

背景与挑战

背景概述

该数据集研究了平原斑马的四种发声类型('snort','soft snort','squeal','quagga quagga')，采用机器学习方法分析声学特征和频谱图，发现不同方法适用于不同发声类型的分类，建议结合使用这两种方法。