Passive acoustic monitoring applied to black-and-white ruffed lemurs (Varecia variegata) in Ranomafana National Park, Madagascar

Data accompanying the paper: <strong>"An integrated passive acoustic monitoring and deep learning pipeline applied to black-and-white ruffed lemurs (\textit{Varecia variegata}) in Ranomafana National Park, Madagascar"</strong> Fieldwork was conducted at Mangevo (21.3833S, 47.4667E), an isolated and undisturbed forest location within Ranomafana National Park (RNP), located in southeastern Madagascar, during the period of May to July 2019. To facilitate passive acoustic monitoring, we deployed a total of two SongMeter SM4 devices (manufactured by Wildlife Acoustics) and two Swift units (provided by the Cornell Yang Center for Conservation Bioacoustics). The placement of these recorders was strategically chosen within the central regions of known subgroups, ensuring a minimum distance of 300 meters between each device. The SongMeter devices operated at a sampling rate of 48 kHz, while the Swift units operated at 32 kHz, respectively, enabling comprehensive audio data collection throughout the study period. We provide the audio data (.wav) used to train and test our neural network classifier along with the corresponding labelled text files (.data). <strong>Files provided</strong> <strong>Test_Audio.zip </strong>-- contains (.wav) testing audio files <strong>Test_Annotations.zip </strong>-- contains (.svl) manually annotated testing files which can be read in using Sonic Visualiser or by parsing the XML file in Python or another programming language. Load in the audio file into Sonic Visualiser and then drag-and-drop the corresponding .svl file. <strong>Training_Audio_batch_x.zip -</strong>- several .zip files were created to simplify downloading. There are 10 batches, each is roughly 4GB. Each batch contains (.wav) training audio files <strong>Training_Annotations.zip</strong> -- contains (.svl) manually annotated training files which can be read in using Sonic Visualiser or by parsing the XML file in Python or another programming language. Load in the audio file into Sonic Visualiser and then drag-and-drop the corresponding .svl file. <strong>model_weights_tensorflow.hdf5 </strong>-- the Tensorflow model. Load the model using: model = tf.keras.models.load_model(model_filepath) note that the model expects a three channel input as explained in the research article.本数据集配套于论文：<strong>"集成被动声学监测与深度学习流水线（deep learning pipeline）应用于马达加斯加拉努马法纳国家公园的黑白领狐猴（*Varecia variegata*）"</strong> 野外工作于2019年5月至7月期间，在位于马达加斯加东南部的拉努马法纳国家公园（Ranomafana National Park, RNP）内一处与世隔绝且未受干扰的森林区域曼热沃（Mangevo，21.3833S, 47.4667E）开展。 为开展被动声学监测（passive acoustic monitoring），本研究共部署2台由野生动物声学公司（Wildlife Acoustics）制造的SongMeter SM4声学记录仪，以及2台由康奈尔杨保护生物声学中心（Cornell Yang Center for Conservation Bioacoustics）提供的Swift单元。记录仪的布设点位经策略性选择，均位于已知狐猴群的核心区域，且各设备间最小间距不低于300米。 其中SongMeter设备的采样率为48 kHz，Swift单元的采样率为32 kHz，确保了整个研究周期内的音频数据全面采集。 本数据集包含用于训练与测试神经网络分类器的音频数据（.wav格式）及其对应的标注文本文件（.data格式）。 <strong>已提供文件</strong> <strong>Test_Audio.zip</strong>——包含测试用音频文件（.wav格式） <strong>Test_Annotations.zip</strong>——包含手动标注的测试集标注文件（.svl格式），可通过Sonic Visualiser软件读取，或通过Python等编程语言解析其XML格式文件。使用时需将音频文件导入Sonic Visualiser后，拖拽对应的.svl文件即可加载标注。 <strong>Training_Audio_batch_x.zip</strong>——为简化下载流程，本数据集拆分为10个批次的压缩包，每个批次大小约4GB，每个压缩包均包含训练用音频文件（.wav格式） <strong>Training_Annotations.zip</strong>——包含手动标注的训练集标注文件（.svl格式），可通过Sonic Visualiser软件读取，或通过Python等编程语言解析其XML格式文件。使用时需将音频文件导入Sonic Visualiser后，拖拽对应的.svl文件即可加载标注。 <strong>model_weights_tensorflow.hdf5</strong>——TensorFlow模型权重文件。可通过以下代码加载模型：`model = tf.keras.models.load_model(model_filepath)`。需注意，该模型要求输入为三通道数据，具体说明详见研究论文。