  Contrastive learning enables learning useful audio and speech representations
without ground-truth labels by maximizing the similarity between latent
representations of similar signal segments. In this framework various data
augmentation techniques are usually exploited to help enforce desired
invariances within the learned representations, improving performance on
various audio tasks thanks to more robust embeddings. Now, selecting the most
relevant augmentations has proven crucial for better downstream performances.
Thus, this work introduces a conditional independance-based method which allows
for automatically selecting a suitable distribution on the choice of
augmentations and their parametrization from a set of predefined ones, for
contrastive self-supervised pre-training. This is performed with respect to a
downstream task of interest, hence saving a costly hyper-parameter search.
Experiments performed on two different downstream tasks validate the proposed
approach showing better results than experimenting without augmentation or with
baseline augmentations. We furthermore conduct a qualitative analysis of the
automatically selected augmentations and their variation according to the
considered final downstream dataset.
