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