Using a Joint-Embedding Predictive Architecture for Symbolic Music Understanding
Rafik Hachana ⋅ Bader Rasheed
2025 Poster
in
Workshop: Artificial Intelligence for Music: Where Creativity Meets Computation
in
Workshop: Artificial Intelligence for Music: Where Creativity Meets Computation
Abstract
We adapt Joint-Embedding Predictive Architectures to symbolic music by adding new masking methods and loss terms. The model achieves competitive results with much less training compute on a few downstream tasks, while falling short on a few others. This is mainly attributed to a bias towards positional information in the learned representation.
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