  Taking advantage of the structure of large datasets to pre-train Deep
Learning models is a promising strategy to decrease the need for supervised
data. Self-supervised learning methods, such as contrastive and its variation
are a promising way towards obtaining better representations in many Deep
Learning applications. Soundscape ecology is one application in which
annotations are expensive and scarce, therefore deserving investigation to
approximate methods that do not require annotations to those that rely on
supervision. Our study involves the use of the methods Barlow Twins and VICReg
to pre-train different models with the same small dataset with sound patterns
of bird and anuran species. In a downstream task to classify those animal
species, the models obtained results close to supervised ones, pre-trained in
large generic datasets, and fine-tuned with the same task.
