  This paper addresses the problem of self-supervised general-purpose audio
representation learning. We explore the use of Joint-Embedding Predictive
Architectures (JEPA) for this task, which consists of splitting an input
mel-spectrogram into two parts (context and target), computing neural
representations for each, and training the neural network to predict the target
representations from the context representations. We investigate several design
choices within this framework and study their influence through extensive
experiments by evaluating our models on various audio classification
benchmarks, including environmental sounds, speech and music downstream tasks.
We focus notably on which part of the input data is used as context or target
and show experimentally that it significantly impacts the model's quality. In
particular, we notice that some effective design choices in the image domain
lead to poor performance on audio, thus highlighting major differences between
these two modalities.
