Self-Supervised Learning of Audio Representations From Permutations With Differentiable Ranking
Self-Supervised Learning of Audio Representations From Permutations With Differentiable Ranking
Self-supervised pre-training using so-called "pretext" tasks has recently shown impressive performance across a wide range of modalities. In this work, we advance self-supervised learning from permutations, by pre-training a model to reorder shuffled parts of the spectrogram of an audio signal, to improve downstream classification performance. We make two main …