Pretrained audio models show large performance gaps between standard MIR tasks and music recommendation in both hot and cold-start settings.
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Separating acoustic and expectation ANN representations as teacher targets improves EEG music identification beyond baselines and seed ensembles.
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.
citing papers explorer
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Adopting State-of-the-Art Pretrained Audio Representations for Music Recommender Systems
Pretrained audio models show large performance gaps between standard MIR tasks and music recommendation in both hot and cold-start settings.
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Expectation and Acoustic Neural Network Representations Enhance Music Identification from Brain Activity
Separating acoustic and expectation ANN representations as teacher targets improves EEG music identification beyond baselines and seed ensembles.
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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.