New framework brings interpretability to music auto-tagging with multimodal features
Researchers combine signal processing, deep learning, and ontologies to explain tagging decisions.
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A team led by Andreas Patakis introduces a semantic-aware interpretable multimodal framework for music auto-tagging. Unlike opaque foundation models, this approach combines features from signal processing, deep learning, ontology engineering, and NLP, then clusters them semantically. An expectation maximization algorithm assigns distinct weights to each feature group based on its contribution to tagging, enabling researchers and end-users to understand why a tag was assigned.
The method achieves competitive performance with existing black-box models while providing clear interpretability. Accepted at Interspeech 2025, the paper paves the way for more transparent and user-centric music tagging systems, crucial for digital libraries and recommender systems where trust and explainability are increasingly important.
- Uses multiple modalities: signal processing, deep learning, ontologies, and NLP features
- Clusters features semantically and applies expectation maximization for weight assignment
- Accepted at Interspeech 2025, achieving competitive performance with interpretable outputs
Why It Matters
Transparent music tagging builds trust for users and researchers, improving digital library organization and recommendation systems.