Extending Music Annotation Schemas: Zero-Shot Prediction or Few-Shot Adaptation?

The paper investigates how to extend music annotation schemas when new attributes need to be added to commercial music catalogs. It compares zero‑shot prediction using audio‑language models, learning from pretrained representations, and supervised adaptation on existing annotations, using a benchmark built on the MGPHot dataset. The findings show that supervised adaptation outperforms zero‑shot prediction even with limited annotation budgets, while reusing frozen representations remains the best option for very modest budgets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• llms
• benchmarks