We propose an automatic data processing pipeline to extract vocal productions from large-scale natural audio recordings. Through a series of computational steps (windowing, creation of a noise class, data augmentation, re-sampling, transfer learning, Bayesian optimisation), it automatically trains a neural network for detecting various types of natural vocal productions in a noisy data stream without requiring a large sample of labeled data. We test it on two different data sets, one from a group of Guinea baboons recorded from a primate research center and one from human babies recorded at home. The pipeline trains a model on 72 and 77 minutes of labeled audio recordings, with an accuracy of 94.58% and 99.76%. It is then used to process 443 and 174 hours of natural continuous recordings and it creates two new databases of 38.8 and 35.2 hours, respectively. We discuss the strengths and limitations of this approach that can be applied to any massive audio recording.

翻译：我们提出了一种自动数据处理流水线，用于从大规模自然音频录音中提取发声内容。通过一系列计算步骤（包括窗口化、噪声类别创建、数据增强、重采样、迁移学习和贝叶斯优化），该方法可在无需大量标注数据样本的情况下，自动训练神经网络以检测含噪数据流中的各类自然发声。我们在两个不同数据集上进行了测试：一个来自灵长类研究中心记录的几内亚狒狒群体，另一个来自家庭环境中记录的人类婴儿。该流水线分别利用72分钟和77分钟的标注音频数据训练模型，准确率分别达到94.58%和99.76%。随后将其用于处理443小时和174小时的自然连续录音，并分别构建了38.8小时和35.2小时的两个新数据库。我们讨论了该方法（可适用于任何大规模音频记录）的优势与局限性。