arXiv:2505.17233v3 Announce Type: replace Abstract: Music auto-tagging is essential for organizing and discovering music in extensive digital libraries. While foundation models achieve exceptional performance in this domain, their outputs often lack interpretability, limiting trust and usability for researchers and end-users alike. In this work, we present an interpretable framework for music auto-tagging that leverages groups of musically meaningful multimodal features, derived from signal processing, deep learning, ontology engineering, and natural language processing. To enhance interpretab
The proliferation of foundation models in music auto-tagging necessitates solutions for interpretability to build trust and increase usability.
Improving the interpretability of AI models, particularly in creative domains like music, is crucial for wider adoption, ethical development, and effective human-AI collaboration.
This work introduces a framework that makes music auto-tagging more transparent by leveraging multimodal features, moving towards more explicable AI systems.
- · AI ethicists
- · Music streaming services
- · AI developers
- · Music researchers
- · Black-box AI models
- · Manual music tagging
- · Developers ignoring interpretability
Increased user trust and adoption of AI-driven music management tools due to clear explanations of auto-tagging decisions.
Development of more sophisticated and nuanced AI models that inherently prioritize interpretability as a core design principle.
Potential for new creative tools that allow musicians and producers to interact with AI-generated tags and recommendations on a deeper, semantically meaningful level.
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Read at arXiv cs.LG