  We propose an automatic data processing pipeline to extract vocal productions
from large-scale natural audio recordings and classify these vocal productions.
The pipeline is based on a deep neural network and adresses both issues
simultaneously. Though a series of computationel steps (windowing, creation of
a noise class, data augmentation, re-sampling, transfer learning, Bayesian
optimisation), it automatically trains a neural network without requiring a
large sample of labeled data and important computing resources. Our end-to-end
methodology can handle noisy recordings made under different recording
conditions. We test it on two different natural audio 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.
