FIGMA proposes a multi-view contrastive architecture plus the FGMCaps dataset to retrieve music from fine-grained textual descriptions of musical attributes, reporting up to 73.3% relative gains over CLAP baselines.
InProceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017), pages 316–323
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TASTE dataset and MuQ-token aggregation enable effective use of audio features from large music models to improve content-based music recommendations over collaborative filtering alone.
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FIGMA: Towards FIne-Grained Music retrievAl
FIGMA proposes a multi-view contrastive architecture plus the FGMCaps dataset to retrieve music from fine-grained textual descriptions of musical attributes, reporting up to 73.3% relative gains over CLAP baselines.
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Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models
TASTE dataset and MuQ-token aggregation enable effective use of audio features from large music models to improve content-based music recommendations over collaborative filtering alone.