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 ..., Data collection and sampling
We collected data in three locations, in Denmark and South Africa: 1) 10 months between December 2020 and July 2021 and between September and December 2021, at Pilanesberg National Park (hereafter “PNP”), South Africa, covering both dry season (i.e. from May to September) and wet season (i.e. from October to April) (1); 2) 16 days between May and June 2019, and 33 days between February and May 2022, at Knuthenborg Safari Park (hereafter “KSP”), Denmark, covering both periods before the park’s opening for tourists (i.e. from November to March) and after (i.e. from April to October); 3) 4 days in August 2019 at Givskud Zoo (hereafter “GKZ”), Denmark.
For all places and periods, three types of data were collected as follows: 1) Pictures were taken for each individual from both sides using a camera (Nikon COOLPIX P950); 2) Contexts of vocal production were recorded either through notes (in the first period of KSP and in GKZ) or videos (in the second period of KS..., , # Vocalizations in the plains zebra (*Equus quagga*)

### **Data and Scripts**

* **1_Praat_Script_Zebra_Vocalisations.praat**: This script is used to extract vocal features using the software Praat.
* **2_Data_Script_Vocal_Repertoire.zip**: This archive contains data and scripts for analyzing the vocal repertoire. It includes two folders:
* **Feature_based_analyses**:
* The dataset \"feature_based_input.csv\" is the input for both scripts in this folder.
* \"feature_based_supervised_classification_xgboost.ipynb\" is used for supervised analysis.
* \"feature_based_unsupervised_clustering.ipynb\" is used for unsupervised analysis.
* **Spectrogram_based_analyses**:
* The \"spectrogram_based_classification\" folder contains the input data \"calltype_spec.npz\" and \"calltype_y.csv\", as well as the notebook script \"spectrogram_based_classification_cnn.ipynb\" for supervised machine learning analysis.
* The \"spectrogram_based_clustering\" folder contains subfolders \"audio\", \"data\"...