Guinea baboon vocalizations dataset automatically extracted with a deep neural network from natural audio recordings

<strong>Abstract</strong> The data collection process consisted of continuously recording during one month a group of Guinea baboons living in semi-liberty at the CNRS primatology center in Rousset-sur-Arc (France). Two microphones we placed nearby their enclosure to continuously record the sounds produced by the group. A convolutional neural network (CNN) was used on these large and noisy audio recordings to automatically extract segments of sound containing a baboon vocal production by following the method of Bonafos et al. (2023). The resulting dataset consists of one-second to several-minute wav files of automatically detected vocalizations segments. The dataset thus provides a wide range of baboon vocalizations produced at all times of the day. It can be used to study vocal productions of non-human primates, their repertoire, their distribution over the day, their frequency, and their heterogeneity. In addition to the analysis of animal communication, the dataset can also be used as a learning base for sound classification models. <strong>Data acquisition</strong> The data are audio recordings of baboons. The recordings were made with a H6 Zoom recorder, using the included XYH-6 stereo microphone. The sample size is 44100 Hertz, 16 bits. The microphones were placed in the vicinity of the enclosure for one month and recorded continuously on a PC computer. A CNN passed over the data with a sliding window of 1 second and an overlap of 80% to detect the vocal productions of the baboons. The dataset consists of the segments predicted by the CNN to contain a baboon vocalization. Windows containing signal less than one second apart were merged into a single vocalization. <strong>Data source location</strong> Institution: CNRS, Primate Facility City/Town/Region: Rousset-sur-Arc Country: France Latitude and longitude for collected samples/data: 43.47033535251509, 5.6514732876668905 <strong>Value of the data</strong> This dataset is relatively unique in terms of the quantity of vocalizations available. This massive dataset can be very useful to two types of scientific communities: experts in primatology who study the vocal productions of non-human primates, and experts in data science and audio signal processing. The machine learning research community has at its disposal a database of several dozen hours of animal vocalizations, which will make it possible to build up a large learning base, very useful for Environemental Sound Recognition tasks, for example. <strong>Objective</strong> This dataset is a follow-up of two studies on the vocal productions of Guinea baboons (Papio papio) in which we carried out analyses of their vocal productions on the basis of a relatively large vocalization sample containing around 1300 vocalizations (Boë, Berthommier, Legou, Captier, Kemp, Sawallis, Becker, Rey, &amp; Fagot, 2017; Kemp, Rey, Legou, Boë, Berthommier, Becker, &amp; Fagot, 2017). The aim was to collect a larger database using the technique of deep convolutional neural networks in order to 1) automatically detect vocal productions in a large continuous audio recording and 2) perform a categorization of these vocalizations on a more massive sample. A description of the pipeline that enabled these automatic detections and categorizations is given in Bonafos, Pudlo, Freyermuth, Legou, Fagot, Tronçon, &amp; Rey (2023). <strong>Data description</strong> The data is a set of audio files in wav format. They are at least one second long (the size of the window), up to several minutes, if several windows are consecutively predicted as containing signal. Moreover, we add the labeled data we used to train the CNN which did the prediction. We also provide two hours of the continuous recordings to have an idea of the continuous recordings and test the code of the paper provided on gitlab. In addition, there is a database in csv format listing all the vocalizations, the day and time of their production, and the prediction probabilities of the model. <strong>Experimental design, materials and methods</strong> The original recordings represent one month of continuous audio recording. Seven hours of this month were manually labelled. They were segmented and labelled according to whether or not there was a monkey vocalization (i.e., noise or vocalization) and, if there was a vocalization, according to the type of vocalization (6 possible classes: bark, copulation grunt, grunt, scream, yak, wahoo). These manually labelled data were used as a training set for a CNN, which was automatically trained following the pipeline of Bonafos et al. (2023). This model was then used to automatically detect and classify vocalization during the whole month of audio recording. It processes the data in the same way when predicting new data as it does when training. It uses a sliding window of one second with an overlap of 80%. It does not take into account information from previous predictions, but calculates the probability of a vocalization in each one-second window independently. It then iterates through the month. For each window, the model predicts two outputs: the probability that there is a vocalization and the probability of each class of vocalization. For the purpose of generating the wav files, if a window has a probability of a vocalization greater than 0.5, it is considered to contain a vocalization. If it is the first one, a vocalization is started at that moment. If the time windows that follow a vocalization also contain a vocalization, then the signal they contain is added to the first segment for which a vocalization has been detected. As soon as a one-second segment no longer contains a signal corresponding to a vocalization, the wav file is closed. If windows are predicted to contain no vocalizations, but are between two windows that contain vocalizations within 1 second of each other, then all windows are merged.**摘要** 本数据集的采集流程为：在法国阿尔克河畔鲁塞市的法国国家科学研究中心（CNRS）灵长类动物研究中心，对半自由圈养的一群几内亚狒狒进行为期一个月的连续录音。我们在其圈舍附近布设2个麦克风，持续记录该群体发出的声音。随后采用卷积神经网络（Convolutional Neural Network, CNN）针对这些体量庞大且背景噪音繁多的音频录制文件，按照Bonafos等人(2023)的方法，自动提取包含狒狒发声片段。最终生成的数据集包含时长从1秒至数分钟的自动检测到的狒狒发声片段。该数据集涵盖了一天中各个时段记录的丰富多样的狒狒发声，可用于研究非人灵长类的发声行为、发声库、日间分布、发生频率以及发声异质性。除动物通讯分析外，本数据集还可作为声音分类模型的学习基准数据集。 **数据采集** 本数据集为狒狒的音频录制文件。录制采用H6 Zoom录音机搭配附带的XYH-6立体声麦克风，采样率为44100 Hz，位深度为16比特。麦克风被布置在圈舍周边，连续录制时长一个月，数据存储于个人电脑中。我们使用卷积神经网络（CNN）以1秒滑动窗口、80%重叠率的方式遍历数据，以检测狒狒的发声。数据集由CNN预测为包含狒狒发声的片段即为最终数据。若两个间隔小于1秒的发声窗口将合并为单个发声片段。 **数据来源地点** 机构：CNRS（法国国家科学研究中心）灵长类动物研究中心 城市/地区：阿尔克河畔鲁塞市 国家：法国 采样数据的经纬度：43.47033535251509, 5.6514732876668905 **数据价值** 就可用发声样本体量而言，本数据集相对稀缺。该超大规模数据集可服务于两类科研群体：研究非人灵长类发声行为的灵长动物学专家，以及数据科学与音频信号处理领域的专家。机器学习研究社区可获得数十小时量级的动物发声数据库，这将助力构建大型学习基准，例如在环境声音识别（Environmental Sound Recognition）任务中极具应用价值。 **研究目标** 本数据集是两项关于几内亚狒狒（*Papio papio*）发声行为研究的后续工作：此前两项研究基于约1300条发声样本的相对较大样本量，对其发声行为进行了分析（Boë et al., 2017; Kemp et al., 2017）。本研究旨在通过深度卷积神经网络技术收集规模更大的数据库，以实现两个目标：1）在大规模连续音频录制中自动检测发声行为；2）基于更庞大的样本量对这些发声进行分类。关于实现上述自动检测与分类流程的详细说明参见Bonafos et al. (2023)。 **数据描述** 本数据集包含一组wav格式音频文件，单文件时长至少为1秒（滑动窗口大小），若连续多个窗口均被预测为包含发声信号时，文件时长可达数分钟。此外，我们还提供了用于训练上述预测CNN的带标注数据，以及2小时的连续录制音频文件，以便研究者了解原始连续录制效果并测试GitLab上发布的论文配套代码。同时，数据集还包含一份csv格式数据库，其中列出了所有发声片段、发声日期与时间，以及模型预测的发声概率。 **实验设计、材料与方法** 原始录制数据为为期一个月的连续音频记录。其中7小时的录制数据被人工标注：研究人员将音频分割并标注为“存在狒狒发声”或“仅背景噪音”，若存在发声，则进一步标注发声类型（共6类：吠叫、交配低吼、低吼、尖叫、yak、wahoo）。这些人工标注数据被用作CNN的训练集，按照Bonafos et al. (2023)的流程自动完成训练。随后，该模型被用于在整月的音频录制数据中自动检测并分类狒狒发声。模型在预测新数据时沿用与训练阶段一致的处理流程：采用1秒滑动窗口、80%重叠率，且不依赖先前的预测结果，独立计算每个1秒窗口内存在发声的概率。随后遍历整月的音频数据，每个窗口会输出两个预测结果：存在发声的概率，以及各发声类别的概率。为生成wav文件，若某窗口的发声概率大于0.5，则认为该窗口包含发声。若为第一个符合条件的窗口，则在此刻创建一个新的发声片段。若后续的时间窗口同样包含发声，则将其信号并入已检测到的发声片段中。当某一秒窗口不再包含对应发声的信号时，关闭该wav文件。若两个包含发声的窗口之间间隔不足1秒时，即使中间存在被预测为不含发声的窗口，则将所有相关窗口合并为一个片段。