  This paper explores the automated process of determining stem compatibility
by identifying audio recordings of single instruments that blend well with a
given musical context. To tackle this challenge, we present Stem-JEPA, a novel
Joint-Embedding Predictive Architecture (JEPA) trained on a multi-track dataset
using a self-supervised learning approach.
  Our model comprises two networks: an encoder and a predictor, which are
jointly trained to predict the embeddings of compatible stems from the
embeddings of a given context, typically a mix of several instruments. Training
a model in this manner allows its use in estimating stem compatibility -
retrieving, aligning, or generating a stem to match a given mix - or for
downstream tasks such as genre or key estimation, as the training paradigm
requires the model to learn information related to timbre, harmony, and rhythm.
  We evaluate our model's performance on a retrieval task on the MUSDB18
dataset, testing its ability to find the missing stem from a mix and through a
subjective user study. We also show that the learned embeddings capture
temporal alignment information and, finally, evaluate the representations
learned by our model on several downstream tasks, highlighting that they
effectively capture meaningful musical features.
